diff --git a/atomsci/ddm/docs/PARAMETERS.md b/atomsci/ddm/docs/PARAMETERS.md
index 135b332b..611ae5c6 100644
--- a/atomsci/ddm/docs/PARAMETERS.md
+++ b/atomsci/ddm/docs/PARAMETERS.md
@@ -276,6 +276,14 @@ The AMPL pipeline contains many parameters and options to fit models and make pr
 |*Description:*|True/False flag for setting verbosity|
 |*Default:*|FALSE|
 |*Type:*|Bool|
+
+- **seed**  
+  
+|||
+|-|-|
+|*Description:*|Seed used for initializing a random number generator to ensure results are reproducible. Default is None and a random seed will be generated.|
+|*Default:*|None|
+|*Type:*|int|
   
 - **production**  
   
@@ -529,6 +537,30 @@ the model will train for max_epochs regardless of validation error.|
 |*Default:*|scaffold|
 |*Type:*|str|
   
+- **sampling_method**  
+  
+|||
+|-|-|
+|*Description:*|The sampling method for addressing class imbalance in classification datasets. Options include 'undersampling' and 'SMOTE'.|
+|*Default:*|None|
+|*Type:*|str|
+
+- **sampling_ratio**  
+  
+|||
+|-|-|
+|*Description:*|The desired ratio of the minority class to the majority class after sampling (e.g., if str, 'minority', 'not minority'; if float, '0.2', '1.0'). |
+|*Default:*|auto|
+|*Type:*|str|
+
+- **sampling_k_neighbors**  
+  
+|||
+|-|-|
+|*Description:*|The number of nearest neighbors to consider when generating synthetic samples (e.g., 5, 7, 9). Specifically used for SMOTE sampling method.|
+|*Default:*|5|
+|*Type:*|int|
+
 - **mtss\_num\_super\_scaffolds**  
   
 |||
diff --git a/atomsci/ddm/pipeline/GeneticAlgorithm.py b/atomsci/ddm/pipeline/GeneticAlgorithm.py
index 0f6eb11a..53e3d967 100644
--- a/atomsci/ddm/pipeline/GeneticAlgorithm.py
+++ b/atomsci/ddm/pipeline/GeneticAlgorithm.py
@@ -1,10 +1,10 @@
 import numpy as np
+import uuid
 import scipy.spatial.distance as scipy_distance
 import multiprocessing
-import random
 from tqdm import tqdm
 import timeit
-from typing import Any, Callable, List, Tuple
+from typing import Any, Callable, List, Tuple, Optional
 
 N_PROCS = multiprocessing.cpu_count()
 
@@ -22,7 +22,8 @@ def __init__(self,
                 init_pop: List[List[Any]],
                 fitness_func: Callable,
                 crossover_func: Callable,
-                mutate_func: Callable):
+                mutate_func: Callable,
+                seed: Optional[int]):
         """
         Creates a GeneticAlgorithm object
 
@@ -40,8 +41,14 @@ def __init__(self,
         mutate_func: Callable
             A callable that takes a list of chromosomes and returns another list of mutated 
             chromosomes
+        seed: Optional[int]
+            Seed for random number generator
         """
 
+        if seed is None:
+            seed = uuid.uuid4().int % (2**32)
+        self.random_state = np.random.default_rng(seed)
+
         self.pop = init_pop
         self.pop_scores = None
         self.num_pop = len(init_pop)
@@ -177,13 +184,13 @@ def step(self, print_timings: bool = False):
 
         # select parents using rank selection
         i = timeit.default_timer()
-        new_pop = self.crossover_func(parents, self.num_pop)
+        new_pop = self.crossover_func(parents, self.num_pop, random_state=self.random_state)
         if print_timings:
             print('\tcrossover %0.2f min'%((timeit.default_timer()-i)/60))
 
         # mutate population
         i = timeit.default_timer()
-        self.pop = self.mutate_func(new_pop)
+        self.pop = self.mutate_func(new_pop, random_state=self.random_state)
         if print_timings:
             print('\tmutate %0.2f min'%((timeit.default_timer()-i)/60))
             print('total %0.2f min'%((timeit.default_timer()-start)/60))
@@ -199,23 +206,23 @@ def step(self, print_timings: bool = False):
     def fitness_func(chromosome):
         return 1 - scipy_distance.rogerstanimoto(chromosome, target_chromosome)
 
-    def crossover_func(parents, pop_size):
+    def crossover_func(parents, pop_size, random_state):
         new_pop = []
         for i in range(num_pop):
             parent1 = parents[i%len(parents)]
             parent2 = parents[(i+1)%len(parents)]
 
-            crossover_point = random.randint(0, len(parents[0])-1)
+            crossover_point = random_state.integers(0, len(parents[0])-1, 1)[0]
             new_pop.append(parent1[:crossover_point]+parent2[crossover_point:])
 
         return new_pop
 
-    def mutate_func(pop, mutate_chance=0.01):
+    def mutate_func(pop, random_state, mutate_chance=0.01):
         new_pop = []
         for chromosome in pop:
             new_chromosome = list(chromosome)
             for i, g in enumerate(new_chromosome):
-                if random.random() < mutate_chance:
+                if random_state.random() < mutate_chance:
                     if new_chromosome[i] == 0:
                         new_chromosome[i] = 1
                     else:
diff --git a/atomsci/ddm/pipeline/MultitaskScaffoldSplit.py b/atomsci/ddm/pipeline/MultitaskScaffoldSplit.py
index e3432360..37dd7a34 100644
--- a/atomsci/ddm/pipeline/MultitaskScaffoldSplit.py
+++ b/atomsci/ddm/pipeline/MultitaskScaffoldSplit.py
@@ -1,6 +1,5 @@
 import argparse
 import logging
-import random
 import timeit
 import tempfile
 from typing import List, Optional, Set, Tuple
@@ -636,8 +635,8 @@ def split(self,
             A tuple with 3 elements that are training, validation, and test compound
             indices into dataset, respectively
         """
-        if seed is not None:
-            np.random.seed(seed)
+        self.seed = seed
+
         self.dataset = dataset
         self.diff_fitness_weight_tvt = diff_fitness_weight_tvt
         self.diff_fitness_weight_tvv = diff_fitness_weight_tvv
@@ -674,7 +673,7 @@ def split(self,
             population.append(split_chromosome)
 
         gene_alg = ga.GeneticAlgorithm(population, self.grade, ga_crossover,
-                        ga_mutate)
+                        ga_mutate, self.seed)
         #gene_alg.iterate(num_generations)
         for i in range(self.num_generations):
             gene_alg.step(print_timings=print_timings)
@@ -859,7 +858,8 @@ def train_valid_test_split(self,
         return train_dataset, valid_dataset, test_dataset
 
 def ga_crossover(parents: List[List[str]],
-                num_pop: int) -> List[List[str]]:
+                num_pop: int,
+                random_state: np.random.Generator) -> List[List[str]]:
     """Create the next generation from parents
 
     A random index is chosen and genes up to that index from
@@ -872,6 +872,8 @@ def ga_crossover(parents: List[List[str]],
         A list of chromosomes.
     num_pop: int
         The number of new chromosomes to make
+    random_state: np.random.Generator
+        Random number generator
     Returns
     -------
     List[List[str]]
@@ -883,13 +885,14 @@ def ga_crossover(parents: List[List[str]],
         parent1 = parents[i%len(parents)]
         parent2 = parents[(i+1)%len(parents)]
 
-        crossover_point = random.randint(0, len(parents[0])-1)
+        crossover_point = random_state.integers(low=0, high=len(parents[0])-1, size=1)[0]
         new_pop.append(parent1[:crossover_point]+parent2[crossover_point:])
 
     return new_pop
 
 def ga_mutate(new_pop: List[List[str]],
-            mutation_rate: float = .02) -> List[List[str]]:
+            random_state: np.random.Generator,
+            mutation_rate: float = .02,) -> List[List[str]]:
     """Mutate the population
 
     Each chromosome is copied and mutated at mutation_rate.
@@ -900,6 +903,8 @@ def ga_mutate(new_pop: List[List[str]],
     ----------
     new_pop: List[List[str]]
         A list of chromosomes.
+    random_state: np.random.Generator
+        Random number generator
     mutation_rate: float
         How often a mutation occurs. 0.02 is a good rate for
         my test sets.
@@ -912,8 +917,8 @@ def ga_mutate(new_pop: List[List[str]],
     for solution in new_pop:
         new_solution = list(solution)
         for i, gene in enumerate(new_solution):
-            if random.random() < mutation_rate:
-                new_solution[i] = ['train', 'valid', 'test'][random.randint(0,2)]
+            if random_state.random() < mutation_rate:
+                new_solution[i] = ['train', 'valid', 'test'][random_state.integers(low=0, high=2, size=1)[0]]
         mutated.append(new_solution)
 
     return mutated
@@ -1039,6 +1044,7 @@ def parse_args():
     parser.add_argument('id_col', type=str, help='the column containing ids')
     parser.add_argument('response_cols', type=str, help='comma seperated string of response columns')
     parser.add_argument('output', type=str, help='name of the split file')
+    parser.add_argument('seed', type=int, default=0, help='Random seed used in random number generators.')
 
     return parser.parse_args()
 
@@ -1054,5 +1060,6 @@ def parse_args():
     mss = MultitaskScaffoldSplitter()
     mss_split_df = split_with(total_df, mss, 
         smiles_col=args.smiles_col, id_col=args.id_col, response_cols=response_cols, 
-        diff_fitness_weight=dfw, ratio_fitness_weight=rfw, num_generations=args.num_gens)
+        diff_fitness_weight=dfw, ratio_fitness_weight=rfw, num_generations=args.num_gens,
+        seed=args.seed)
     mss_split_df.to_csv(args.output, index=False)
diff --git a/atomsci/ddm/pipeline/model_datasets.py b/atomsci/ddm/pipeline/model_datasets.py
index 8ebd7aa8..baf0406d 100644
--- a/atomsci/ddm/pipeline/model_datasets.py
+++ b/atomsci/ddm/pipeline/model_datasets.py
@@ -395,7 +395,7 @@ def get_featurized_data(self, params=None):
                 if params.prediction_type=='classification':
                     w = w.astype(np.float32)
 
-                self.untransformed_dataset = NumpyDataset(features, self.vals, ids=ids)
+                self.update_untransformed_responses(ids, self.vals)
                 self.dataset = NumpyDataset(features, self.vals, ids=ids, w=w)
                 self.log.info("Using prefeaturized data; number of features = " + str(self.n_features))
                 return
@@ -421,7 +421,7 @@ def get_featurized_data(self, params=None):
         self.log.debug("Number of features: " + str(self.n_features))
            
         # Create the DeepChem dataset       
-        self.untransformed_dataset = NumpyDataset(features, self.vals, ids=ids)
+        self.update_untransformed_responses(ids, self.vals)
         self.dataset = NumpyDataset(features, self.vals, ids=ids, w=w)
         # Checking for minimum number of rows
         if len(self.dataset) < params.min_compound_number:
@@ -451,7 +451,7 @@ def get_dataset_tasks(self, dset_df):
         return self.tasks is not None
 
     # ****************************************************************************************
-    def split_dataset(self):
+    def split_dataset(self, random_state=None, seed=None):
         """Splits the dataset into paired training/validation and test subsets, according to the split strategy
                 selected by the model params. For traditional train/valid/test splits, there is only one training/validation
                 pair. For k-fold cross-validation splits, there are k different train/valid pairs; the validation sets are
@@ -470,7 +470,7 @@ def split_dataset(self):
 
         # Create object to delegate splitting to.
         if self.splitting is None:
-            self.splitting = split.create_splitting(self.params)
+            self.splitting = split.create_splitting(self.params, random_state=random_state, seed=seed)
         self.train_valid_dsets, self.test_dset, self.train_valid_attr, self.test_attr = \
             self.splitting.split_dataset(self.dataset, self.attr, self.params.smiles_col)
         if self.train_valid_dsets is None:
@@ -497,6 +497,12 @@ def _check_classes(self):
             (Boolean): boolean specifying if all classes are specified in all splits
         """
         ref_class_set = get_classes(self.train_valid_dsets[0][0].y)
+        num_classes = len(ref_class_set)
+        if num_classes != self.params.class_number:
+            logger = logging.getLogger('ATOM')
+            logger.warning(f"Expected class_number:{self.params.class_number} "
+                           f"classes but got {num_classes} instead. Double check "
+                           "response columns or class_number parameter.")
         for train, valid in self.train_valid_dsets:
             if not ref_class_set == get_classes(train.y):
                 return False
@@ -581,7 +587,7 @@ def create_dataset_split_table(self):
         return split_df
 
     # ****************************************************************************************
-    def load_presplit_dataset(self, directory=None):
+    def load_presplit_dataset(self, directory=None, random_state=None, seed=None):
         """Loads a table of compound IDs assigned to split subsets, and uses them to split
         the currently loaded featurized dataset.
 
@@ -608,7 +614,7 @@ def load_presplit_dataset(self, directory=None):
         """
 
         # Load the split table from the datastore or filesystem
-        self.splitting = split.create_splitting(self.params)
+        self.splitting = split.create_splitting(self.params, random_state=random_state, seed=seed)
 
         try:
             split_df, split_kv = self.load_dataset_split_table(directory)
@@ -673,11 +679,31 @@ def combined_training_data(self):
         # All of the splits have the same combined train/valid data, regardless of whether we're using
         # k-fold or train/valid/test splitting.
         if self.combined_train_valid_data is None:
+            # normally combining one fold is sufficient, but if SMOTE or undersampling is being used
+            # just combining the first fold isn't enough
             (train, valid) = self.train_valid_dsets[0]
             combined_X = np.concatenate((train.X, valid.X), axis=0)
             combined_y = np.concatenate((train.y, valid.y), axis=0)
             combined_w = np.concatenate((train.w, valid.w), axis=0)
             combined_ids = np.concatenate((train.ids, valid.ids))
+
+            if self.params.sampling_method=='SMOTE' or self.params.sampling_method=='undersampling':
+                # for each successive fold, merge in any new compounds
+                # this loop just won't run if there are no additional folds
+                for train, valid in self.train_valid_dsets[1:]:
+                    fold_ids = np.concatenate((train.ids, valid.ids))
+                    new_id_indexes = [i for i in range(len(fold_ids)) if i not in combined_ids]
+
+                    fold_ids = fold_ids[new_id_indexes]
+                    fold_X = np.concatenate((train.X, valid.X), axis=0)[new_id_indexes]
+                    fold_y = np.concatenate((train.y, valid.y), axis=0)[new_id_indexes]
+                    fold_w = np.concatenate((train.w, valid.w), axis=0)[new_id_indexes]
+
+                    combined_X = np.concatenate((combined_X, fold_X), axis=0)
+                    combined_y = np.concatenate((combined_y, fold_y), axis=0)
+                    combined_w = np.concatenate((combined_w, fold_w), axis=0)
+                    combined_ids = np.concatenate((combined_ids, fold_ids))
+
             self.combined_train_valid_data = NumpyDataset(combined_X, combined_y, w=combined_w, ids=combined_ids)
         return self.combined_train_valid_data
 
@@ -729,6 +755,24 @@ def get_subset_responses_and_weights(self, subset):
 
     # *************************************************************************************
 
+    def update_untransformed_responses(self, ids, y):
+        """
+        Updates self.untransformed_response_dict with the given ids and y
+
+        Parameters:
+        ids (list or np.ndarray): List or array of IDs for which to retrieve untransformed response values.
+
+        y (list or np.ndarray): List or array of responses values.
+
+        Returns:
+        None
+        """
+        self.untransformed_response_dict.update(
+            dict(zip(ids, y))
+            )
+
+    # *************************************************************************************
+
     def get_untransformed_responses(self, ids):
         """
         Returns a numpy array of untransformed response values for the given IDs.
@@ -740,9 +784,8 @@ def get_untransformed_responses(self, ids):
         np.ndarray: A numpy array of untransformed response values corresponding to the given IDs.
         """        
 
-        response_vals = np.zeros((len(ids), self.untransformed_dataset.y.shape[1]))
-        if len(self.untransformed_response_dict) == 0:
-            self.untransformed_response_dict = dict(zip(self.untransformed_dataset.ids, self.untransformed_dataset.y))
+        num_tasks = len(self.untransformed_response_dict[ids[0]])
+        response_vals = np.zeros((len(ids), num_tasks))
 
         for i, id in enumerate(ids):
             response_vals[i] = self.untransformed_response_dict[id]
@@ -803,6 +846,7 @@ def __init__(self, params, featurization, contains_responses=False):
         self.tasks = None
         self.attr = None
         self.contains_responses = contains_responses
+        self.untransformed_response_dict = {}
 
     # ****************************************************************************************
     def get_dataset_tasks(self, dset_df):
@@ -867,7 +911,7 @@ def get_featurized_data(self, dset_df, is_featurized=False):
             self.log.warning("Done")
         self.n_features = self.featurization.get_feature_count()
         
-        self.untransformed_dataset= NumpyDataset(features, self.vals, ids=ids)
+        self.update_untransformed_responses(ids, self.vals)
         self.dataset = NumpyDataset(features, self.vals, ids=ids)
 
     # ****************************************************************************************
@@ -944,6 +988,7 @@ def __init__(self, params, featurization=None, ds_client=None):
 
         super().__init__(params, featurization)
         self.dataset_oid = None
+        self.untransformed_response_dict = {}
         if params.dataset_name:
             self.dataset_name = params.dataset_name
         else:
diff --git a/atomsci/ddm/pipeline/model_pipeline.py b/atomsci/ddm/pipeline/model_pipeline.py
index 9ac2aa33..e6c5e4e2 100644
--- a/atomsci/ddm/pipeline/model_pipeline.py
+++ b/atomsci/ddm/pipeline/model_pipeline.py
@@ -30,6 +30,8 @@
 from atomsci.ddm.pipeline import parameter_parser as parse
 from atomsci.ddm.pipeline import model_tracker as trkr
 from atomsci.ddm.pipeline import transformations as trans
+from atomsci.ddm.pipeline import random_seed as rs
+from atomsci.ddm.pipeline import sampling as sample
 
 logging.basicConfig(format='%(asctime)-15s %(message)s')
 
@@ -179,7 +181,7 @@ class ModelPipeline:
             data (ModelDataset object): A data object that featurizes and splits the dataset
     """
 
-    def __init__(self, params, ds_client=None, mlmt_client=None):
+    def __init__(self, params, ds_client=None, mlmt_client=None, random_state=None, seed=None):
         """Initializes ModelPipeline object.
 
         Args:
@@ -213,6 +215,23 @@ def __init__(self, params, ds_client=None, mlmt_client=None):
         self.log = logging.getLogger('ATOM')
         self.run_mode = 'training'  # default, can be overridden later
         self.start_time = time.time()
+        
+        # initialize seed
+        if seed is None:
+            seed = getattr(params, 'seed', None)
+            self.random_gen = rs.RandomStateGenerator(params, seed)
+            self.seed = self.random_gen.get_seed()
+        else:
+            # pass the seed into the RandomStateGenerator
+            self.random_gen = rs.RandomStateGenerator(seed)
+            self.seed = self.random_gen.get_seed()
+        
+        if random_state is None:
+            self.random_state = self.random_gen.get_random_state()
+        else:
+            self.random_state = random_state
+        # log the seed used 
+        self.log.info('Initiating ModelPipeline with seed {}'.format(self.seed))
 
         # Default dataset_name parameter from dataset_key
         if params.dataset_name is None:
@@ -273,6 +292,7 @@ def load_featurize_data(self, params=None):
         Args:
             params (Namespace): Optional set of parameters to be used for featurization; by default this function
             uses the parameters used when the pipeline was created.
+            seed (int): Optional seed for reproducibility
 
         Side effects:
             Sets the following attributes of the ModelPipeline
@@ -291,12 +311,24 @@ def load_featurize_data(self, params=None):
                 self.log.info('Training in production mode. Ignoring '
                     'previous split and creating production split. '
                     'Production split will not be saved.')
-                self.data.split_dataset()
-            elif not (params.previously_split and self.data.load_presplit_dataset()):
-                self.data.split_dataset()
+                self.data.split_dataset(random_state=self.random_state, seed=self.seed)
+            elif not (params.previously_split and self.data.load_presplit_dataset(random_state=self.random_state, seed=self.seed)):
+                self.data.split_dataset(random_state=self.random_state, seed=self.seed)
                 self.data.save_split_dataset()
+                # write split metadata
+                self.create_split_metadata()
+                self.save_split_metadata()
             if self.data.params.prediction_type == 'classification':
                 self.data._validate_classification_dataset()
+
+        # apply sampling before fitting transformers
+        if self.run_mode == 'training':
+            for i, (train, valid) in enumerate(self.data.train_valid_dsets):
+                if self.data.params.prediction_type == 'classification' and self.params.sampling_method is not None:
+                    train = sample.apply_sampling_method(train, params, random_state=self.random_state, seed=self.seed)
+                    self.data.update_untransformed_responses(train.ids, train.y)
+                    self.data.train_valid_dsets[i] = (train, valid)
+
         # We now create transformers after splitting, to allow for the case where the transformer
         # is fitted to the training data only. The transformers are then applied to the training,
         # validation and test sets separately.
@@ -360,6 +392,13 @@ def create_model_metadata(self):
             hyperparam_uuid=self.params.hyperparam_uuid,
             ampl_version=mu.get_ampl_version()
         )
+        # add in sampling method parameters for documentation/reproducibility
+        if self.params.sampling_method is not None:
+            model_params['sampling_method'] = self.params.sampling_method
+        if self.params.sampling_ratio is not None:
+            model_params['sampling_ratio'] = self.params.sampling_ratio
+        if self.params.sampling_k_neighbors is not None:
+            model_params['sampling_k_neighbors'] = self.params.sampling_k_neighbors
 
         splitting_metadata = self.data.get_split_metadata()
         model_metadata = dict(
@@ -378,6 +417,8 @@ def create_model_metadata(self):
             model_metadata[key] = data
         for key, data in trans.get_transformer_specific_metadata(self.params).items():
             model_metadata[key] = data
+            
+        model_metadata['seed'] = self.seed
 
         self.model_metadata = model_metadata
 
@@ -431,6 +472,28 @@ def save_model_metadata(self, retries=5, sleep_sec=60):
             trkr.save_model_tarball(self.output_dir, self.params.model_tarball_path)
         self.model_wrapper._clean_up_excess_files(self.model_wrapper.model_dir)
 
+   # ****************************************************************************************
+    def create_split_metadata(self):
+        """Creates metadata for each split dataset. 
+        It will save the seed used to create the split dataset and relevant parameters."""
+        self.split_data = dict(
+            dataset_key = self.params.dataset_key,
+            id_col = self.params.id_col, 
+            smiles_col = self.params.smiles_col, 
+            response_cols = self.params.response_cols,
+            seed = self.seed
+        )
+        self.splitting_metadata = self.data.get_split_metadata() 
+        self.split_data['splitting_metadata'] = self.splitting_metadata
+
+    # ****************************************************************************************
+    def save_split_metadata(self):
+        out_file = os.path.join(self.output_dir, 'split_metadata.json')
+ 
+        with open(out_file, 'w') as out:
+            json.dump(self.split_data, out, sort_keys=True, indent=4, separators=(',', ': '))
+            out.write("\n")
+
     # ****************************************************************************************
     def create_prediction_metadata(self, prediction_results):
         """Initializes a data structure to hold performance metrics from a model run on a new dataset,
@@ -592,7 +655,7 @@ def train_model(self, featurization=None):
 
         ## create model wrapper if not split_only
         if not self.params.split_only:
-            self.model_wrapper = model_wrapper.create_model_wrapper(self.params, self.featurization, self.ds_client)
+            self.model_wrapper = model_wrapper.create_model_wrapper(self.params, self.featurization, self.ds_client, random_state=self.random_state, seed=self.seed)
             self.model_wrapper.setup_model_dirs()
 
         self.load_featurize_data()
@@ -1089,7 +1152,9 @@ def run_models(params, shared_featurization=None, generator=False):
 
         # Create the ModelWrapper object.
         pipeline.model_wrapper = model_wrapper.create_model_wrapper(pipeline.params, featurization,
-                                                                    pipeline.ds_client)
+                                                                    pipeline.ds_client,
+                                                                    random_state=pipeline.random_state,
+                                                                    seed=pipeline.seed)
 
         # Get the tarball containing the saved model from the datastore, and extract it into model_dir.
         model_dataset_oid = metadata_dict['model_parameters']['model_dataset_oid']
@@ -1181,7 +1246,9 @@ def regenerate_results(result_dir, params=None, metadata_dict=None, shared_featu
     # Create the ModelWrapper object.
 
     pipeline.model_wrapper = model_wrapper.create_model_wrapper(pipeline.params, featurization,
-                                                                pipeline.ds_client)
+                                                                pipeline.ds_client,
+                                                                random_state=pipeline.random_state,
+                                                                seed=pipeline.seed)
     # Get the tarball containing the saved model from the datastore, and extract it into model_dir (old format)
     # or output_dir (new format) according to the format of the tarball contents.
 
@@ -1409,7 +1476,9 @@ def create_prediction_pipeline_from_file(params, reload_dir, model_path=None, mo
     pipeline.orig_params = orig_params
 
     # Create the ModelWrapper object.
-    pipeline.model_wrapper = model_wrapper.create_model_wrapper(pipeline.params, featurization)
+    pipeline.model_wrapper = model_wrapper.create_model_wrapper(pipeline.params, featurization,
+                                                                random_state=pipeline.random_state,
+                                                                seed=pipeline.seed)
 
     orig_log_level = pipeline.log.getEffectiveLevel()
     if verbose:
diff --git a/atomsci/ddm/pipeline/model_wrapper.py b/atomsci/ddm/pipeline/model_wrapper.py
index 59e515b6..2c068f81 100644
--- a/atomsci/ddm/pipeline/model_wrapper.py
+++ b/atomsci/ddm/pipeline/model_wrapper.py
@@ -168,7 +168,7 @@ def all_bases(model):
     return result
 
 # ****************************************************************************************
-def create_model_wrapper(params, featurizer, ds_client=None):
+def create_model_wrapper(params, featurizer, ds_client=None, random_state=None, seed=None):
     """Factory function for creating Model objects of the correct subclass for params.model_type.
 
     Args:
@@ -186,11 +186,11 @@ def create_model_wrapper(params, featurizer, ds_client=None):
     """
     if params.model_type == 'NN':
         if params.featurizer == 'graphconv':
-            return GraphConvDCModelWrapper(params, featurizer, ds_client)
+            return GraphConvDCModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
         else:
-            return MultitaskDCModelWrapper(params, featurizer, ds_client)
+            return MultitaskDCModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
     elif params.model_type == 'RF':
-        return DCRFModelWrapper(params, featurizer, ds_client)
+        return DCRFModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
     elif params.model_type == 'xgboost':
         if not xgboost_supported:
             raise Exception("Unable to import xgboost. \
@@ -206,9 +206,9 @@ def create_model_wrapper(params, featurizer, ds_client=None):
                              installation: \
                              from pip: pip install xgboost==0.90")
         else:
-            return DCxgboostModelWrapper(params, featurizer, ds_client)
+            return DCxgboostModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
     elif params.model_type == 'hybrid':
-        return HybridModelWrapper(params, featurizer, ds_client)
+        return HybridModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
     elif params.model_type in pp.model_wl:
         requested_model = pp.model_wl[params.model_type]
         bases = all_bases(requested_model)
@@ -217,9 +217,9 @@ def create_model_wrapper(params, featurizer, ds_client=None):
         if any(['TorchModel' in str(b) for b in bases]):
             if not afp_supported:
                 raise Exception("dgl and dgllife packages must be installed to use attentive_fp model.")
-            return PytorchDeepChemModelWrapper(params, featurizer, ds_client)
+            return PytorchDeepChemModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
         elif any(['KerasModel' in str(b) for b in bases]):
-            return KerasDeepChemModelWrapper(params, featurizer, ds_client)
+            return KerasDeepChemModelWrapper(params, featurizer, ds_client, random_state=random_state, seed=seed)
     else:
         raise ValueError("Unknown model_type %s" % params.model_type)
 
@@ -254,7 +254,7 @@ class ModelWrapper(object):
             best_model_dir (str): The subdirectory under output_dir that contains the best model. Created in setup_model_dirs
 
     """
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes ModelWrapper object.
 
         Args:
@@ -298,6 +298,9 @@ def __init__(self, params, featurizer, ds_client):
         self.transformers_x = trans.get_blank_transformations()
         self.transformers_w = trans.get_blank_transformations()
 
+        self.random_state = random_state
+        self.seed = seed
+
         # ****************************************************************************************
 
     def setup_model_dirs(self):
@@ -1220,7 +1223,7 @@ class HybridModelWrapper(NNModelWrapper):
 
     """
 
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes HybridModelWrapper object.
 
         Args:
@@ -1245,7 +1248,8 @@ def __init__(self, params, featurizer, ds_client):
 
             model: dc.models.TorchModel
         """
-        super().__init__(params, featurizer, ds_client)
+        super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
+
         if self.params.layer_sizes is None:
             if self.params.featurizer == 'ecfp':
                 self.params.layer_sizes = [1000, 500]
@@ -1650,7 +1654,7 @@ class ForestModelWrapper(ModelWrapper):
 
     contains code that is similar between the two tree based classes
     """
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes DCRFModelWrapper object.
 
         Args:
@@ -1659,7 +1663,7 @@ def __init__(self, params, featurizer, ds_client):
             featurizer (Featurization): Object managing the featurization of compounds
             ds_client: datastore client.
         """
-        super().__init__(params, featurizer, ds_client)
+        super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
         self.best_model_dir = os.path.join(self.output_dir, 'best_model')
         self.model_dir = self.best_model_dir
         os.makedirs(self.best_model_dir, exist_ok=True)
@@ -1860,7 +1864,7 @@ class DCRFModelWrapper(ForestModelWrapper):
 
     """
 
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes DCRFModelWrapper object.
 
         Args:
@@ -1869,7 +1873,7 @@ def __init__(self, params, featurizer, ds_client):
             featurizer (Featurization): Object managing the featurization of compounds
             ds_client: datastore client.
         """
-        super().__init__(params, featurizer, ds_client)
+        super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
 
     # ****************************************************************************************
     def make_dc_model(self, model_dir):
@@ -1887,12 +1891,14 @@ def make_dc_model(self, model_dir):
             rf_model = RandomForestRegressor(n_estimators=self.params.rf_estimators,
                                              max_features=self.params.rf_max_features,
                                              max_depth=self.params.rf_max_depth,
-                                             n_jobs=-1)
+                                             n_jobs=-1,
+                                             random_state=self.seed)
         else:
             rf_model = RandomForestClassifier(n_estimators=self.params.rf_estimators,
                                               max_features=self.params.rf_max_features,
                                               max_depth=self.params.rf_max_depth,
-                                              n_jobs=-1)
+                                              n_jobs=-1,
+                                              random_state=self.seed)
 
         return dc.models.sklearn_models.SklearnModel(rf_model, model_dir=model_dir)
 
@@ -2012,7 +2018,7 @@ class DCxgboostModelWrapper(ForestModelWrapper):
 
     """
 
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes RunModel object.
 
         Args:
@@ -2021,7 +2027,7 @@ def __init__(self, params, featurizer, ds_client):
             featurizer (Featurization): Object managing the featurization of compounds
             ds_client: datastore client.
         """
-        super().__init__(params, featurizer, ds_client)
+        super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
 
     # ****************************************************************************************
     def make_dc_model(self, model_dir):
@@ -2052,7 +2058,7 @@ def make_dc_model(self, model_dir):
                                          reg_lambda=1,
                                          scale_pos_weight=1,
                                          base_score=0.5,
-                                         random_state=0,
+                                         random_state= self.seed,
                                          missing=np.nan,
                                          importance_type='gain',
                                          n_jobs=-1,
@@ -2077,7 +2083,7 @@ def make_dc_model(self, model_dir):
                                           reg_lambda=1,
                                           scale_pos_weight=1,
                                           base_score=0.5,
-                                          random_state=0,
+                                          random_state=self.seed,
                                           importance_type='gain',
                                           missing=np.nan,
                                           gpu_id = -1,
@@ -2190,7 +2196,7 @@ def reload_model(self, reload_dir):
                                          reg_lambda=1,
                                          scale_pos_weight=1,
                                          base_score=0.5,
-                                         random_state=0,
+                                         random_state=self.seed,
                                          missing=np.nan,
                                          importance_type='gain',
                                          n_jobs=-1,
@@ -2215,7 +2221,7 @@ def reload_model(self, reload_dir):
                                          reg_lambda=1,
                                          scale_pos_weight=1,
                                          base_score=0.5,
-                                         random_state=0,
+                                         random_state=self.seed, 
                                          importance_type='gain',
                                          missing=np.nan,
                                          gpu_id = -1,
@@ -2368,7 +2374,7 @@ class PytorchDeepChemModelWrapper(NNModelWrapper):
             valid_perfs (dict): A dictionary of predicted values and metrics on the validation dataset
 
     """
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes AttentiveFPModelWrapper object. Creates the underlying DeepChem AttentiveFPModel instance.
 
         Args:
@@ -2378,9 +2384,8 @@ def __init__(self, params, featurizer, ds_client):
             ds_client: datastore client.
         """
         # use NNModelWrapper init. 
-        super().__init__(params, featurizer, ds_client)
+        super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
         self.num_epochs_trained = 0
-
         self.model = self.recreate_model()
 
     # ****************************************************************************************
@@ -2406,6 +2411,7 @@ def recreate_model(self, **kwargs):
 
         # build the model
         model = chosen_model(
+                sed = self.seed,
                 **extracted_features
             ) 
 
@@ -2736,7 +2742,7 @@ class GraphConvDCModelWrapper(KerasDeepChemModelWrapper):
 
     """
 
-    def __init__(self, params, featurizer, ds_client):
+    def __init__(self, params, featurizer, ds_client, random_state=None, seed=None):
         """Initializes GraphConvDCModelWrapper object.
 
         Args:
@@ -2764,12 +2770,11 @@ def __init__(self, params, featurizer, ds_client):
             model: The dc.models.GraphConvModel, MultitaskRegressor, or MultitaskClassifier object, as specified by the params attribute
 
         """
-        super().__init__(params, featurizer, ds_client)
+        super().__init__(params, featurizer, ds_client, random_state=random_state, seed=seed)
         # TODO (ksm): The next two attributes aren't used; suggest we drop them.
         self.g = tf.Graph()
         self.sess = tf.compat.v1.Session(graph=self.g)
         self.num_epochs_trained = 0
-
         self.model = self.recreate_model(model_dir=self.model_dir)
 
     # ****************************************************************************************
@@ -2807,7 +2812,8 @@ def recreate_model(self, model_dir=None):
             dense_layer_size=self.params.layer_sizes[-1],
             dropout=self.params.dropouts,
             penalty=self.params.weight_decay_penalty,
-            penalty_type=self.params.weight_decay_penalty_type)
+            penalty_type=self.params.weight_decay_penalty_type,
+            seed=self.seed)
         return model
 
     # ****************************************************************************************
diff --git a/atomsci/ddm/pipeline/parameter_parser.py b/atomsci/ddm/pipeline/parameter_parser.py
index 7e3b4e0c..c7b1a3e4 100644
--- a/atomsci/ddm/pipeline/parameter_parser.py
+++ b/atomsci/ddm/pipeline/parameter_parser.py
@@ -537,7 +537,7 @@ def get_list_args(self):
                          }
 convert_to_int_list = {'layer_sizes','rf_max_features','rf_estimators', 'rf_max_depth',
                        'umap_dim', 'umap_neighbors', 'layer_nums', 'node_nums',
-                       'xgb_max_depth',  'xgb_n_estimators'}.union(all_auto_int_lists())
+                       'xgb_max_depth',  'xgb_n_estimators', 'seed'}.union(all_auto_int_lists())
 convert_to_numeric_list = convert_to_float_list | convert_to_int_list
 keep_as_list = {'dropouts','weight_init_stddevs','bias_init_consts',
                 'layer_sizes','dropout_list','layer_nums'}.union(all_auto_lists())
@@ -1038,6 +1038,10 @@ def get_parser():
         '--verbose', dest='verbose', action='store_true',
         help='True/False flag for setting verbosity')
     parser.set_defaults(verbose=False)
+    parser.add_argument(
+        '--seed', dest='seed', default=None,
+        help='Random seed used for initializing the random number generator to ensure results are reproducible.'
+        'Default is None and a random seed will be generated.')
 
     # **********************************************************************************************************
     # model_building_parameters: graphconv
@@ -1224,6 +1228,19 @@ def get_parser():
         help='Type of splitter to use: index, random, scaffold, butina, ave_min, temporal, fingerprint, multitaskscaffold or stratified.'
              ' Used to set the splitting.py subclass. Can be input as a comma separated list for hyperparameter search'
              ' (e.g. \'scaffold\',\'random\')')
+    # sampling specific parameters (imbalance-learn)
+    parser.add_argument(
+        '--sampling_method', dest='sampling_method', type=str, default=None,
+        help='Method for sampling to address class imbalance (e.g., \'undersampling\', \'SMOTE\')')
+    
+    parser.add_argument(
+        '--sampling_ratio', dest='sampling_ratio', type=str, default='auto',
+        help='The "sampling_ratio" parameter of SMOTE must be a float in the range (0.0, 1.0], a str '
+            'among {"auto", "not majority", "minority", "all", "not minority"}')
+    parser.add_argument(
+        '--sampling_k_neighbors', dest='sampling_k_neighbors', type=int, default=5,
+        help='The nearest neighbors used to define the neighborhood of samples to use to generate the synthetic samples. Specifically used for SMOTE.')
+
 
     parser.add_argument(
         '--mtss_num_super_scaffolds', default=40, type=int,
@@ -1731,8 +1748,37 @@ def postprocess_args(parsed_args):
     if vars(parsed_args).get('dataset_key') and os.path.exists(parsed_args.dataset_key):
         _ = mto.many_to_one(fn=parsed_args.dataset_key, smiles_col=parsed_args.smiles_col, id_col=parsed_args.id_col)
 
+    # Validates the sampling_ratio argument for SMOTE and undersampling
+    parsed_args.sampling_ratio = validate_sampling_strategy_argument(parsed_args.sampling_ratio)
+
     return parsed_args
 
+#***********************************************************************************************************
+def validate_sampling_strategy_argument(value):
+    """Validates sampling_strategy parameter for SMOTE and undersampling.
+    Validates that the input value is either a float in the range (0.0, 1.0] or a string among 
+    {'auto', 'not majority', 'minority', 'all', 'not minority'}. Raises a ValueError if the validation fails.
+
+    Args:
+        value (str): The input value to validate.
+
+    Raises:
+        ValueError: If the value is not a float in the range (0.0, 1.0] or a valid string.
+    """
+    valid_strings = {"auto", "not majority", "minority", "all", "not minority"}
+    
+    try:
+        float_value = float(value)
+        if float_value <= 0.0 or float_value > 1.0:
+            raise ValueError(f"Value '{value}' is not a float in the range (0.0, 1.0].")
+        else:
+            return float_value
+    except ValueError:
+        if value not in valid_strings:
+            raise ValueError(f"Value '{value}' is not a valid string among {valid_strings}.")
+        else:
+            return value
+
 
 #***********************************************************************************************************
 def make_dataset_key_absolute(parsed_args):
diff --git a/atomsci/ddm/pipeline/perf_data.py b/atomsci/ddm/pipeline/perf_data.py
index 727958af..a8931c5a 100644
--- a/atomsci/ddm/pipeline/perf_data.py
+++ b/atomsci/ddm/pipeline/perf_data.py
@@ -210,28 +210,6 @@ def __init__(self, model_dataset, subset):
         self.model_score = None
         self.weights = None
 
-    # ****************************************************************************************
-    def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-    # ****************************************************************************************
-    def get_pred_values(self):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-    # ****************************************************************************************
-    def compute_perf_metrics(self, per_task=False):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-
     # ****************************************************************************************
     # class RegressionPerfData
     def model_choice_score(self, score_type='r2'):
@@ -399,28 +377,6 @@ def __init__(self, model_dataset, subset):
         self.model_score = None
         self.weights = None
 
-    # ****************************************************************************************
-    def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-    # ****************************************************************************************
-    def get_pred_values(self):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-    # ****************************************************************************************
-    def compute_perf_metrics(self, per_task=False):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-
     # ****************************************************************************************
     # class HybridPerfData
     def model_choice_score(self, score_type='r2'):
@@ -623,22 +579,7 @@ def __init__(self, model_dataset, subset):
         self.perf_metrics = []
         self.model_score = None
         self.weights = None
-
-    # ****************************************************************************************
-    def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-    # ****************************************************************************************
-    def get_pred_values(self):
-        """Raises:
-            NotImplementedError: The method is implemented by subclasses
-        """
-        raise NotImplementedError
-
-
+        
     # ****************************************************************************************
     # class ClassificationPerfData
     def model_choice_score(self, score_type='roc_auc'):
@@ -658,8 +599,8 @@ def model_choice_score(self, score_type='roc_auc'):
 
         """
         ids, pred_classes, class_probs, prob_stds = self.get_pred_values()
-        real_vals = self.get_real_values()
-        weights = self.get_weights()
+        real_vals = self.get_real_values(ids=ids)
+        weights = self.get_weights(ids=ids)
         scores = []
             
         for i in range(self.num_tasks):
@@ -997,7 +938,11 @@ def get_pred_values(self):
             otherwise.
 
         """
-        ids = sorted(self.pred_vals.keys())
+        #ids = sorted(self.pred_vals.keys())
+        all_ids = sorted(self.pred_vals.keys())
+        # with kfold + SMOTE, not all ids have predictions 
+        ids = [id for id in all_ids if not (self.pred_vals[id].size == 0)]
+
         if self.subset in ['train', 'test', 'train_valid']:
             vals = np.concatenate([self.pred_vals[id].mean(axis=0, keepdims=True).reshape((1,-1)) for id in ids])
             if self.folds > 1:
@@ -1153,6 +1098,8 @@ def __init__(self, model_dataset, subset, predict_probs=True):
         self.num_cmpds = dataset.y.shape[0]
         self.num_tasks = dataset.y.shape[1]
         self.num_classes = len(set(model_dataset.dataset.y.flatten()))
+        # pred vals maps compound ids to a matrix of predictions.
+        # predictions will be concatentated one by one as they come in in accumulate_preds
         self.pred_vals = dict([(id, np.empty((0, self.num_tasks, self.num_classes), dtype=np.float32)) for id in dataset.ids])
 
         real_vals, self.weights = model_dataset.get_subset_responses_and_weights(self.subset)
@@ -1195,6 +1142,7 @@ def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
         """
         class_probs = self._reshape_preds(predicted_vals)
         for i, id in enumerate(ids):
+            # Record predictions for each compound.
             self.pred_vals[id] = np.concatenate([self.pred_vals[id], class_probs[i,:,:].reshape((1,self.num_tasks,-1))], axis=0)
         self.folds += 1
         real_vals = self.get_real_values(ids=ids)
@@ -1236,7 +1184,10 @@ def get_pred_values(self):
             probability estimates (only available for the 'train' and 'test' subsets; None otherwise).
 
         """
-        ids = sorted(self.pred_vals.keys())
+        all_ids = sorted(self.pred_vals.keys())
+        # with kfold + SMOTE, not all ids have predictions
+        ids = [id for id in all_ids if not (self.pred_vals[id].size == 0)]
+
         if self.subset in ['train', 'test', 'train_valid']:
             class_probs = np.concatenate([self.pred_vals[id].mean(axis=0, keepdims=True)
                                    for id in ids], axis=0)
@@ -1392,7 +1343,7 @@ def __init__(self, model_dataset, subset):
 
     # ****************************************************************************************
     # class SimpleRegressionPerfData
-    def accumulate_preds(self, predicted_vals, ids, pred_stds=None):
+    def accumulate_preds(self, predicted_vals, ids=None, pred_stds=None):
         """Add training, validation or test set predictions to the data structure
         where we keep track of them.
 
diff --git a/atomsci/ddm/pipeline/random_seed.py b/atomsci/ddm/pipeline/random_seed.py
new file mode 100644
index 00000000..847a62ae
--- /dev/null
+++ b/atomsci/ddm/pipeline/random_seed.py
@@ -0,0 +1,66 @@
+""" Used to set random seed from parameter_parser for reproducibility. """
+import numpy as np 
+import uuid 
+import random
+import torch
+import tensorflow as tf
+import logging
+logging.basicConfig(format='%(asctime)-15s %(message)s') 
+#----------------------------------------------------------------------------------
+class RandomStateGenerator:
+    """
+    A class to manage random state and seed generation for reproducible randomness.
+
+    Attributes:
+        params: Additional parameters.
+        seed: The seed for the random state.
+        random_state: The random state generator.
+    """
+    def __init__(self, params=None, seed=None):
+        self.params = params
+        if seed is not None:
+            self.seed = seed
+        elif self.params.seed is not None: 
+            self.seed = self.params.seed
+        else:
+            self.seed = uuid.uuid4().int % (2**32)
+        self.set_seed(self.seed)
+    
+    def set_seed(self, seed):
+        log = logging.getLogger('ATOM')
+        log.warning("The global seed is being set to %d, for reproducibility. Note that this action will synchronize the randonmess across all libraries which may impact the randomness of other parts of the pipeline.", seed)
+        """Set the seed for all relevant libraries."""
+        
+        global _seed, _random_state
+        _seed = seed
+
+        _random_state = np.random.default_rng(_seed)
+        
+        # set seed for numpy
+        np.random.default_rng(_seed)
+        
+        # needed for deepchem
+        np.random.seed(_seed)
+        
+        # set seed for random
+        random.seed(_seed)
+        
+        # set seed for PyTorch
+        torch.manual_seed(_seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(_seed)
+            torch.backends.cudnn.deterministic = True
+            torch.backends.cudnn.benchmark = False
+
+        # set seed for tensorflow 
+        tf.random.set_seed(_seed)
+        
+        self.random_state = _random_state
+
+    def get_seed(self):
+        """Returns the seed when called"""
+        return self.seed
+    
+    def get_random_state(self):
+        """Returns the random state when called"""
+        return self.random_state
\ No newline at end of file
diff --git a/atomsci/ddm/pipeline/sampling.py b/atomsci/ddm/pipeline/sampling.py
new file mode 100644
index 00000000..c1fb28b1
--- /dev/null
+++ b/atomsci/ddm/pipeline/sampling.py
@@ -0,0 +1,55 @@
+"""Module used to perform sampling on classification datasets."""
+import numpy as np
+# sampling specific libraries 
+from imblearn.over_sampling import SMOTE
+from imblearn.under_sampling import RandomUnderSampler
+# deepchem for dataset
+import deepchem as dc
+# =====================================================================================================
+def apply_sampling_method(train, params, random_state=None, seed=None):
+    """
+    Apply a sampling method to a classification dataset when split_strategy=='train_valid_test'
+    
+    Inputs: 
+        - train: DeepChem NumpyDataset with train.X, train.y, train.w, and train.ids
+        - params (NameSpace object): contains all the parameter information.
+    Returns:
+        - train_resampled: a DeepChem NumpyDataset with train.X, train.y, train.w, and train.ids
+    """
+    sampling_ratio = params.sampling_ratio
+          
+    if params.sampling_method=='SMOTE':
+        sampling_k_neighbors = params.sampling_k_neighbors # smote specific parameter 
+        smote=SMOTE(sampling_strategy=sampling_ratio, k_neighbors=sampling_k_neighbors, random_state=seed)
+        X_resampled, y_resampled = smote.fit_resample(train.X, train.y.ravel())
+        y_resampled=y_resampled.reshape(-1, 1)
+        
+        # calculate synthetic weights   
+        num_original = len(train.X)
+        num_synthetic = len(X_resampled)-num_original
+
+        # set the new weights equal to 1
+        average_weight = 1 #np.mean(train.w)
+        synthetic_weights=np.full((num_synthetic,1), average_weight, dtype=np.float64)
+        resampled_weights=np.concatenate([train.w, synthetic_weights])
+        
+        # update the id length with synthetic ids for any newly introduced data 
+        synthetic_ids = [f"synthetic_{i}" for i in range(num_synthetic)]
+        new_ids = np.concatenate([train.ids, synthetic_ids])
+
+    elif params.sampling_method == 'undersampling':
+        undersampler = RandomUnderSampler(sampling_strategy=sampling_ratio, random_state=seed)
+        X_resampled, y_resampled = undersampler.fit_resample(train.X, train.y.ravel())
+        y_resampled=y_resampled.reshape(-1, 1)
+        
+        #adjust weights and ids 
+        resampled_indices = undersampler.sample_indices_
+        resampled_weights = train.w[resampled_indices]
+        new_ids = train.ids[resampled_indices]
+
+    else:
+        raise ValueError(f"Unknown sampling method: {params.sampling_method}. Supported methods are 'SMOTE' and 'undersampling'.")
+    # return a new dc.data.NumpyDataset with the resampled data, the original weights and ids
+    train_resampled= dc.data.NumpyDataset(X_resampled, y_resampled, resampled_weights, new_ids)
+
+    return train_resampled
\ No newline at end of file
diff --git a/atomsci/ddm/pipeline/splitting.py b/atomsci/ddm/pipeline/splitting.py
index d79a9c71..1b8ca52a 100644
--- a/atomsci/ddm/pipeline/splitting.py
+++ b/atomsci/ddm/pipeline/splitting.py
@@ -27,7 +27,7 @@
                 'mtss_train_valid_dist_weight', 'mtss_split_fraction_weight', 'mtss_num_pop',
                 'mtss_response_distr_weight']
 
-def create_splitting(params):
+def create_splitting(params, random_state=None, seed=None):
     """Factory function to create appropriate type of Splitting object, based on dataset parameters
     
     Args:
@@ -43,11 +43,11 @@ def create_splitting(params):
     """
 
     if params.production:
-        return ProductionSplitting(params)
+        return ProductionSplitting(params, random_state=random_state, seed=seed)
     elif params.split_strategy == 'train_valid_test':
-        return TrainValidTestSplitting(params)
+        return TrainValidTestSplitting(params, random_state=random_state, seed=seed)
     elif params.split_strategy == 'k_fold_cv':
-        return KFoldSplitting(params)
+        return KFoldSplitting(params, random_state=random_state, seed=seed)
     else:
         raise Exception("Unknown split strategy %s" % params.split_strategy)
 
@@ -172,7 +172,7 @@ class Splitting(object):
 
     """
 
-    def __init__(self, params):
+    def __init__(self, params, random_state=None, seed=None):
         """Constructor, also serves as a factory method for creating the associated DeepChem splitter object
 
         Args:
@@ -193,9 +193,14 @@ def __init__(self, params):
                 splitter (Deepchem split object): A splitting object of the subtype specified by split
 
         """
+        self.random_state = random_state
+        self.seed = seed
+
         self.params = params
         self.split = params.splitter
-        if params.splitter == 'index':
+        if params.production:
+            self.splitter = ProductionSplitter()
+        elif params.splitter == 'index':
             self.splitter = dc.splits.IndexSplitter()
         elif params.splitter == 'random':
             self.splitter = dc.splits.RandomSplitter()
@@ -280,7 +285,7 @@ class KFoldSplitting(Splitting):
 
     """
 
-    def __init__(self, params):
+    def __init__(self, params, random_state=None, seed=None):
         """Initialization method for KFoldSplitting.
 
                 Sets the following attributes for KFoldSplitting:
@@ -293,9 +298,10 @@ def __init__(self, params):
            num_folds (int): The number of k-fold splits to perform
 
         """
-        super().__init__(params)
+        super().__init__(params, random_state, seed)
         self.num_folds = params.num_folds
 
+
     # ****************************************************************************************
 
     def get_split_prefix(self, parent=''):
@@ -364,13 +370,13 @@ def split_dataset(self, dataset, attr_df, smiles_col):
         # Use DeepChem train_test_split() to select held-out test set; then use k_fold_split on the
         # training set to split it into training/validation folds.
         if self.split == 'butina':
-            train_cv, test, _ = self.splitter.train_valid_test_split(dataset)
+            train_cv, test, _ = self.splitter.train_valid_test_split(dataset, seed=self.seed)
             self.splitter = dc.splits.ScaffoldSplitter()
-            train_cv_pairs = self.splitter.k_fold_split(train_cv, self.num_folds)
+            train_cv_pairs = self.splitter.k_fold_split(train_cv, self.num_folds, seed=self.seed)
         else:
             # TODO: Add special handling for AVE splitter
-            train_cv, test = self.splitter.train_test_split(dataset, frac_train=train_frac)
-            train_cv_pairs = self.splitter.k_fold_split(train_cv, self.num_folds)
+            train_cv, test = self.splitter.train_test_split(dataset, frac_train=train_frac, seed=self.seed)
+            train_cv_pairs = self.splitter.k_fold_split(train_cv, self.num_folds, seed=self.seed)
 
         train_valid_dsets = []
         train_valid_attr = []
@@ -403,7 +409,7 @@ class TrainValidTestSplitting(Splitting):
 
     """
 
-    def __init__(self, params):
+    def __init__(self, params, random_state=None, seed=None):
         """Initialization method for TrainValidTestSplitting.
 
                 Sets the following attributes for TrainValidTestSplitting:
@@ -416,7 +422,7 @@ def __init__(self, params):
            num_folds (int): The number of k-fold splits to perform. In this case, it is always set to 1
 
         """
-        super().__init__(params)
+        super().__init__(params, random_state=random_state, seed=seed)
         self.num_folds = 1
 
     # ****************************************************************************************
@@ -490,11 +496,11 @@ def split_dataset(self, dataset, attr_df, smiles_col):
 
         if self.split == 'butina':
             # Can't use train_test_split with Butina because Butina splits into train and valid sets only.
-            train_valid, test, _ = self.splitter.train_valid_test_split(dataset)
+            train_valid, test, _ = self.splitter.train_valid_test_split(dataset, seed=self.seed)
             self.splitter = dc.splits.ScaffoldSplitter()
             # With Butina splitting, we don't have control over the size of the test set
             train_frac = 1.0 - self.params.split_valid_frac
-            train, valid = self.splitter.train_test_split(train_valid, frac_train=train_frac)
+            train, valid = self.splitter.train_test_split(train_valid, frac_train=train_frac, seed=self.seed)
         elif self.split == 'ave_min':
             # AVEMinSplitter also only does train-valid splits, but at least nested splits seem to work.
             # TODO: Change this if we modify AVE splitter to do 3-way splits internally.
@@ -503,11 +509,11 @@ def split_dataset(self, dataset, attr_df, smiles_col):
             log.info("Performing split for test set")
             train_valid, test, _ = self.splitter.train_valid_test_split(dataset, frac_train=train_valid_frac, 
                                                                         frac_valid=self.params.split_test_frac,
-                                                                        frac_test=0.0)
+                                                                        frac_test=0.0, seed=self.seed)
             log.info("Performing split of training and validation sets")
             train, valid, _ = self.splitter.train_valid_test_split(train_valid, frac_train=train_frac/train_valid_frac, 
                                                                    frac_valid=self.params.split_valid_frac/train_valid_frac,
-                                                                   frac_test=0.0)
+                                                                   frac_test=0.0, seed=self.seed)
             log.info("Results of 3-way split: %d training, %d validation, %d test compounds" % (
                      train.X.shape[0], valid.X.shape[0], test.X.shape[0]))
         elif self.split == 'temporal':
@@ -529,11 +535,12 @@ def split_dataset(self, dataset, attr_df, smiles_col):
                 response_distr_fitness_weight=self.params.mtss_response_distr_weight,
                 num_super_scaffolds=self.params.mtss_num_super_scaffolds,
                 num_pop=self.params.mtss_num_pop,
-                num_generations=self.params.mtss_num_generations)
+                num_generations=self.params.mtss_num_generations,
+                seed=self.seed)
         else:
             train_frac = 1.0 - self.params.split_valid_frac - self.params.split_test_frac
             train, valid, test = self.splitter.train_valid_test_split(dataset, 
-                frac_train=train_frac, frac_valid=self.params.split_valid_frac, frac_test=self.params.split_test_frac)
+                frac_train=train_frac, frac_valid=self.params.split_valid_frac, frac_test=self.params.split_test_frac, seed=self.seed)
 
         # After splitting unique compound_ids or SMILES are expanded 
         train, train_attr = dm.expand_selection(train.ids)
@@ -558,10 +565,11 @@ def split(
 # ****************************************************************************************
 
 class ProductionSplitting(Splitting):
-    def __init__(self, params):
+    def __init__(self, params, random_state=None, seed=None):
         """This Splitting only does one thing and ignores all splitter parameters"""
-        self.splitter = ProductionSplitter()
+        super().__init__(params, random_state=random_state, seed=seed)
         self.split = 'production'
+        self.num_folds = 1
 
     # ****************************************************************************************
     def get_split_prefix(self, parent=''):
@@ -621,7 +629,7 @@ def split_dataset(self, dataset, attr_df, smiles_col):
         dm = DatasetManager(dataset=dataset, attr_df=attr_df, smiles_col=smiles_col,
             needs_smiles=self.needs_smiles())
         dataset = dm.compact_dataset()
-        train, valid, test = self.splitter.train_valid_test_split(dataset)
+        train, valid, test = self.splitter.train_valid_test_split(dataset, seed=self.seed)
         
         # After splitting unique compound_ids or SMILES are expanded 
         train, train_attr = dm.expand_selection(train.ids)
diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/SMOTE_balancing_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/SMOTE_balancing_transformer.json
new file mode 100644
index 00000000..49d7f4ab
--- /dev/null
+++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/SMOTE_balancing_transformer.json
@@ -0,0 +1,31 @@
+{
+        "dataset_key" : "replaced",
+        "datastore" : "False",
+        "uncertainty": "False",
+        "splitter": "scaffold",
+        "split_valid_frac": "0.20",
+        "split_test_frac": "0.20",
+        "split_strategy": "train_valid_test",
+        "prediction_type": "classification",
+        "model_choice_score_type": "roc_auc",
+        "response_cols" : "active",
+        "id_col": "compound_id",
+        "smiles_col" : "rdkit_smiles",
+        "result_dir": "replaced",
+        "system": "LC",
+        "transformers": "True",
+        "model_type": "NN",
+        "featurizer": "computed_descriptors",
+        "descriptor_type": "rdkit_raw",
+        "weight_transform_type": "balancing",
+        "learning_rate": ".0007",
+        "layer_sizes": "512,128",
+        "dropouts": "0.3,0.3",
+        "save_results": "False",
+        "max_epochs": "2",
+        "early_stopping_patience": "2",
+        "verbose": "False",
+        "sampling_method": "SMOTE", 
+        "sampling_ratio": "0.5", 
+        "seed":"0"
+    }
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py b/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py
index f09369e7..b1b156c7 100644
--- a/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py
+++ b/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py
@@ -23,16 +23,49 @@ def test_balancing_transformer():
 
     res_dir = tempfile.mkdtemp()
 
+    print('-=======normal balancing===================================')
     balanced_params = params_w_balan(dset_key, res_dir)
     balanced_weights = make_pipeline_and_get_weights(balanced_params)
     (major_weight, minor_weight), (major_count, minor_count) = np.unique(balanced_weights, return_counts=True)
     assert major_weight < minor_weight
     assert major_count > minor_count
+    print('-==========================================')
 
+    print('-=======no balancing===================================')
     nonbalanced_params = params_wo_balan(dset_key, res_dir)
     nonbalanced_weights = make_pipeline_and_get_weights(nonbalanced_params)
     (weight,), (count,) = np.unique(nonbalanced_weights, return_counts=True)
     assert weight == 1
+    print('-==========================================')
+
+    print('-=======SMOTE balancing===================================')
+    smote_balanced_params = params_w_SMOTE_balan(dset_key, res_dir)
+    smote_balanced_params['sampling_ratio'] = 1
+    print('sampling_ratio: ', smote_balanced_params['sampling_ratio'])
+    smote_balanced_weights = make_pipeline_and_get_weights(smote_balanced_params)
+    # all weights should be the same
+    (weight1,), (count1,)= np.unique(smote_balanced_weights, return_counts=True)
+    print('-==========================================')
+
+    print('-=======SMOTE 0.5 balancing===================================')
+    smote_balanced_params = params_w_SMOTE_balan(dset_key, res_dir)
+    smote_balanced_params['sampling_ratio'] = 0.5
+    smote_balanced_weights = make_pipeline_and_get_weights(smote_balanced_params)
+    (major_weight, minor_weight), (major_count, minor_count) = np.unique(smote_balanced_weights, return_counts=True)
+    # there should be twice as many major class as minor class
+    assert abs((major_weight*2) - minor_weight) < .0001
+    assert abs(major_count - (minor_count * 2)) < .0001
+    print('-==========================================')
+
+def params_w_SMOTE_balan(dset_key, res_dir):
+    # Try with SMOTE with ratio set to .50
+    params = read_params(
+        make_relative_to_file('jsons/SMOTE_balancing_transformer.json'),
+        dset_key,
+        res_dir
+    )
+
+    return params
 
 def test_all_transformers():
     """
@@ -331,6 +364,6 @@ def test_kfold_regression_transformers():
 
 if __name__ == '__main__':
     test_kfold_regression_transformers()
-    #test_kfold_transformers()
-    #test_all_transformers()
-    #test_balancing_transformer()
\ No newline at end of file
+    test_kfold_transformers()
+    test_all_transformers()
+    test_balancing_transformer()
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GCNModel.json b/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GCNModel.json
index df299aee..f173fb3b 100644
--- a/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GCNModel.json
+++ b/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GCNModel.json
@@ -7,6 +7,8 @@
     "data_owner": "username",
     "parser_version":"1.0",
 
+    "seed":"2015821819",
+
     "comment": "Input file",
     "comment": "----------------------------------------",
     "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv",
@@ -44,5 +46,11 @@
 
     "comment": "Results",
     "comment": "----------------------------------------",
-    "result_dir": "result"
+    "result_dir": "result",
+
+    "comment": "Test",
+    "comment": "----------------------------------------",    
+    "comment": "with the seed, the result should be 0.9871578924396036.",
+    "perf_threshold": "0.98"
+
 }
diff --git a/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GraphConvModel.json b/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GraphConvModel.json
index 37af3d78..29277b7c 100644
--- a/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GraphConvModel.json
+++ b/atomsci/ddm/test/integrative/dc_models/reg_config_H1_fit_GraphConvModel.json
@@ -50,5 +50,6 @@
     "comment": "Test",
     "comment": "just needs to run, doesn't need to pass",
     "comment": "----------------------------------------",    
-    "perf_threshold": "0.3"
+    "perf_threshold": "0.3",
+    "seed": "0"
 }
diff --git a/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py b/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py
index 39d057a1..1013b484 100644
--- a/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py
+++ b/atomsci/ddm/test/integrative/dc_models/test_retrain_dc_models.py
@@ -153,7 +153,7 @@ def train_and_predict(train_json_f, prefix='delaney-processed'):
 
     return tar_f
 
-def verify_saved_params(original_json_f, tar_f):
+def verify_saved_params(original_json_f, tar_f, keep_seed=False):
     """compares saved params in a tar file with original json"""
     reload_dir = tempfile.mkdtemp()
     with tarfile.open(tar_f, mode='r:gz') as tar:
@@ -187,9 +187,20 @@ def verify_saved_params(original_json_f, tar_f):
     print(tar_feat_params)
     assert original_feat_params == tar_feat_params
 
-def retrain(tar_f, prefix='H1'):
+    print('-----------------------------------')
+    print('seeds')
+    print(original_pp.seed)
+    print(tar_pp.seed)
+    assert original_pp.seed is not None
+    assert tar_pp.seed is not None
+    if keep_seed:
+        assert original_pp.seed == tar_pp.seed
+    else:
+        assert original_pp.seed != tar_pp.seed
+
+def retrain(tar_f, prefix='H1', keep_seed=False):
     """retrain a model from tar_f"""
-    model = mr.train_model_from_tar(tar_f, 'result')
+    model = mr.train_model_from_tar(tar_f, 'result', keep_seed=keep_seed)
 
     uuid = model.params.model_uuid
     re_tar_f = f'result/{prefix}_curated_fit_model_{uuid}.tar.gz'
@@ -212,7 +223,7 @@ def H1_init():
 
 # Train and Predict
 # -----
-def test_reg_config_H1_fit_AttentiveFPModel():
+def run_test_reg_config_H1_fit_AttentiveFPModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
@@ -223,12 +234,16 @@ def test_reg_config_H1_fit_AttentiveFPModel():
 
     verify_saved_params(json_f, tar_f)
 
-    re_tar_f = retrain(tar_f, 'H1')
+    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f)
+    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
+
+def test_reg_config_H1_fit_AttentiveFPModel():
+    run_test_reg_config_H1_fit_AttentiveFPModel(True)
+    run_test_reg_config_H1_fit_AttentiveFPModel(False)
 
 # -----
-def test_reg_config_H1_fit_GCNModel():
+def run_test_reg_config_H1_fit_GCNModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
@@ -239,12 +254,16 @@ def test_reg_config_H1_fit_GCNModel():
 
     verify_saved_params(json_f, tar_f)
 
-    re_tar_f = retrain(tar_f, 'H1')
+    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f)
+    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
+
+def test_reg_config_H1_fit_GCNModel():
+    run_test_reg_config_H1_fit_GCNModel(True)
+    run_test_reg_config_H1_fit_GCNModel(False)
 
 # -----
-def test_reg_config_H1_fit_MPNNModel():
+def run_test_reg_config_H1_fit_MPNNModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
@@ -255,11 +274,15 @@ def test_reg_config_H1_fit_MPNNModel():
 
     verify_saved_params(json_f, tar_f)
 
-    re_tar_f = retrain(tar_f, 'H1')
+    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f)
+    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
 
-def test_reg_config_H1_fit_GraphConvModel():
+def test_reg_config_H1_fit_MPNNModel():
+    run_test_reg_config_H1_fit_MPNNModel(True)
+    run_test_reg_config_H1_fit_MPNNModel(False)
+
+def run_test_reg_config_H1_fit_GraphConvModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
@@ -270,11 +293,15 @@ def test_reg_config_H1_fit_GraphConvModel():
 
     verify_saved_params(json_f, tar_f)
 
-    re_tar_f = retrain(tar_f, 'H1')
+    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f)
+    verify_saved_params(json_f, re_tar_f, keep_seed=keep_seed)
 
-def test_reg_config_H1_fit_PytorchMPNNModel():
+def test_reg_config_H1_fit_GraphConvModel():
+    run_test_reg_config_H1_fit_GraphConvModel(True)
+    run_test_reg_config_H1_fit_GraphConvModel(False)
+
+def run_test_reg_config_H1_fit_PytorchMPNNModel(keep_seed):
     if not llnl_utils.is_lc_system():
         assert True
         return
@@ -285,9 +312,13 @@ def test_reg_config_H1_fit_PytorchMPNNModel():
 
     verify_saved_params(json_f, tar_f)
 
-    re_tar_f = retrain(tar_f, 'H1')
+    re_tar_f = retrain(tar_f, 'H1', keep_seed=keep_seed)
 
-    verify_saved_params(json_f, re_tar_f)
+    verify_saved_params(json_f, re_tar_f, keep_seed)
+
+def test_reg_config_H1_fit_PytorchMPNNModel():
+    run_test_reg_config_H1_fit_PytorchMPNNModel(True)
+    run_test_reg_config_H1_fit_PytorchMPNNModel(False)
 
 if __name__ == '__main__':
     test_reg_config_H1_fit_PytorchMPNNModel() # Pytorch implementation of MPNNModel
diff --git a/atomsci/ddm/test/integrative/delaney_NN/test_delaney_NN.py b/atomsci/ddm/test/integrative/delaney_NN/test_delaney_NN.py
index c88adfcd..1b9e874f 100644
--- a/atomsci/ddm/test/integrative/delaney_NN/test_delaney_NN.py
+++ b/atomsci/ddm/test/integrative/delaney_NN/test_delaney_NN.py
@@ -103,7 +103,7 @@ def test():
 
     # Check training statistics
     # -------------------------
-    integrative_utilities.training_statistics_file(reload_dir, 'test', 0.6)
+    integrative_utilities.training_statistics_file(reload_dir, 'test', 0.55)
 
     # Make prediction parameters
     # --------------------------
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_NN_ecfp.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_NN_ecfp.json
index 94683dcb..39a7cf99 100644
--- a/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_NN_ecfp.json
+++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_NN_ecfp.json
@@ -37,5 +37,7 @@
 
     "comment": "Results",
     "comment": "----------------------------------------",    
-    "result_dir": "result"
+    "result_dir": "result",
+
+    "seed":"3173915729"
 }
diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/reg_config_delaney_fit_NN_graphconv.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/reg_config_delaney_fit_NN_graphconv.json
index dd5d7fdf..aa8461da 100644
--- a/atomsci/ddm/test/integrative/delaney_Panel/jsons/reg_config_delaney_fit_NN_graphconv.json
+++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/reg_config_delaney_fit_NN_graphconv.json
@@ -40,6 +40,7 @@
 
     "comment": "Test",
     "comment": "----------------------------------------",    
-    "perf_threshold": "0.50"
+    "perf_threshold": "0.50",
 
+    "seed":0
 }
diff --git a/atomsci/ddm/test/integrative/delaney_RF/config_delaney_fit_RF.json b/atomsci/ddm/test/integrative/delaney_RF/config_delaney_fit_RF.json
index 0aaf9a37..0941eb2e 100644
--- a/atomsci/ddm/test/integrative/delaney_RF/config_delaney_fit_RF.json
+++ b/atomsci/ddm/test/integrative/delaney_RF/config_delaney_fit_RF.json
@@ -25,6 +25,7 @@
     "comment": "Model",
     "comment": "----------------------------------------",
     "model_type": "RF",
+    "seed": "0",
 
     "comment": "Results",
     "comment": "----------------------------------------",    
diff --git a/atomsci/ddm/test/integrative/integrative_utilities.py b/atomsci/ddm/test/integrative/integrative_utilities.py
index 8db0c9f1..58afff4f 100644
--- a/atomsci/ddm/test/integrative/integrative_utilities.py
+++ b/atomsci/ddm/test/integrative/integrative_utilities.py
@@ -2,6 +2,7 @@
 import json
 import os
 import shutil
+import pandas as pd
 
 
 def clean_fit_predict():
@@ -85,3 +86,36 @@ def copy_delaney(dest='.'):
             '../test_datasets/delaney-processed.csv'))
 
     shutil.copy(delaney_source, dest)
+
+def extract_seed(metadata_path):
+    with open(metadata_path, 'r') as f:
+        metadata = json.load(f)
+    return metadata.get('seed')
+
+def modify_params_with_seed(pparams, seed):
+    pparams.seed = seed
+    return pparams
+
+def get_test_set(dataset_key, split_csv, id_col):
+    """
+    Read the dataset key and split_uuid to split dataset into split components 
+    
+    Parameters: 
+        - dataset_key: path to csv file of dataset
+        - split_uuid: path to split csv file 
+        - id_col: name of ID column
+    
+    Returns:
+        - train, valid, test dataframe
+    """
+    df = pd.read_csv(dataset_key)
+    split_df=pd.read_csv(split_csv)
+    test_df = df[df[id_col].isin(split_df[split_df['subset']=='test']['cmpd_id'])]
+
+    return test_df
+
+def find_best_test_metric(model_metrics):
+    for metric in model_metrics:
+        if metric['label'] == 'best' and metric['subset']=='test':
+            return metric 
+    return None 
diff --git a/atomsci/ddm/test/integrative/model_retrain/config.json b/atomsci/ddm/test/integrative/model_retrain/config.json
new file mode 100644
index 00000000..f962baf6
--- /dev/null
+++ b/atomsci/ddm/test/integrative/model_retrain/config.json
@@ -0,0 +1,35 @@
+{
+    "comment": "Input file",
+    "comment": "----------------------------------------",
+    "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv",
+    "id_col": "Id",
+    "smiles_col": "smiles",
+    "class_number": "3",
+
+    "comment": "Split",
+    "comment": "----------------------------------------",
+    "splitter": "random",
+
+    "comment": "Prediction Type",
+    "comment": "----------------------------------------",
+    "response_cols": "sol_category",
+    "prediction_type": "classification",
+
+    "comment": "Features",
+    "comment": "----------------------------------------",
+    "featurizer": "ecfp",
+
+    "comment": "Model",
+    "comment": "----------------------------------------",
+    "model_type": "NN",
+    "dropout": ".01,.01,.01",
+    "layer_sizes": "256,50,18",
+    "learning_rate": "0.00007",
+    "max_epochs": "25",
+    
+    "comment": "Training",
+    "comment": "----------------------------------------",
+    "comment": "This regulates how long to train the model",
+    "early_stopping_patience": "2"
+}
+
diff --git a/atomsci/ddm/test/integrative/model_retrain/example.csv b/atomsci/ddm/test/integrative/model_retrain/example.csv
new file mode 100644
index 00000000..92ef31ec
--- /dev/null
+++ b/atomsci/ddm/test/integrative/model_retrain/example.csv
@@ -0,0 +1,751 @@
+Id,smiles,sol_category
+EOS12286,Cc1nc(N2CCN(C(=O)Nc3ccc(F)cc3F)CC2)cc(-n2ccnc2)n1,0
+EOS85869,CCN(CC)[C@H]1CCN(C(=O)Cc2nc(C(C)C)c(C)s2)C1,0
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+EOS95892,O=C(Nc1ccc2c(c1)OC1(CCCC1)O2)N1CCN(C(=O)c2ccco2)CC1,0
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+EOS84718,CN1CCC(N(C)C(=O)c2nn(-c3ccccc3)c(=O)c3ccccc23)CC1,2
+EOS78472,CCc1n[nH]c([C@@H]2CN(C(=O)CCn3ccnc3)CCO2)n1,2
+EOS78345,COc1cc(CN2CCC([C@H](O)c3nccn3C)CC2)cc(OC)c1,2
+EOS4115,CN(C(=O)CC1(c2cccc(C(F)(F)F)c2)CC(=O)N(C2CC2)C1=O)C1CCOC1,2
+EOS73098,CO[C@@H](C)CS(=O)(=O)Nc1ccc2nscc2c1,2
+EOS81365,COc1ccc(-c2nc(CN3CCN(c4nc(C)cs4)CC3)co2)cc1,2
+EOS87658,COc1c(C)cc(CNC[C@H]2CN(C)CCO2)cc1C,2
+EOS55331,O=C(c1ccc(-c2ccccc2)[nH]c1=O)N1CC[C@H]1c1ccccc1,2
+EOS14144,CCNc1cc(N2CCN(C(=O)c3ccc(C)cc3)CC2)nc(C)n1,2
+EOS809,CCc1nn2c(=O)cc(CSCC(=O)Nc3cccc(C)c3)nc2s1,2
+EOS17520,CCN(CC)C(=O)c1ccc2c(c1)N(CC(=O)NCc1ccc(C)cc1)C(=O)CS2,2
+EOS56103,O=c1[nH]c2ccccc2nc1CN1CCC[C@H]1c1cccs1,2
+EOS67188,COc1ccc(C)cc1-n1ccc(C(=O)N2CCOC[C@@H]2C)n1,2
+EOS94960,Cc1ccc2[nH]c([C@H]3CCCN(S(=O)(=O)CCCF)C3)nc2c1,2
+EOS1652,COCCNC(=O)c1cc2n(n1)C[C@@H](NC(=O)C1(c3ccc(OC)cc3)CC1)C2,2
+EOS49107,Cc1[nH]c(=O)[nH]c1C(=O)N1C[C@H](COc2ccccc2)OC(C)(C)C1,2
+EOS49879,Cc1noc([C@H](C)N2CCN(c3cc(Cl)ccc3C#N)CC2)n1,2
+EOS49048,COc1ccccc1N1CCN(C(=O)Nc2cccc3cccnc23)CC1,2
+EOS16429,CNC(=O)N1CCC(C2CCN(C(=O)c3cccc4[nH]ccc34)C2)CC1,2
+EOS63304,CCCc1n[nH]c([C@@H]2CN(C(=O)c3cnccn3)CCO2)n1,2
+EOS72950,O=C(CCc1ccc(=O)[nH]c1)N[C@H]1[C@@H]2CN(Cc3ccccc3)C[C@@H]21,2
+EOS14232,CCS(=O)(=O)N1CCN(c2ccc(Nc3cccc(C)n3)nn2)CC1,2
+EOS74664,O=C(Nc1ccccc1C(=O)Nc1ccc(Cl)cn1)c1ccon1,2
+EOS91830,Cc1noc2nc(C(C)C)cc(C(=O)N3CCOC[C@@]3(C)C3CC3)c12,2
+EOS90365,CCOc1ccc(C(=O)N[C@@H](C)c2cccs2)cc1OCC,2
+EOS5048,COc1ccc([C@@H]2CN(C(=O)CCc3ccncc3)[C@@H]3C4CCN(CC4)[C@H]23)cc1,2
+EOS70509,CN1CCN(C(=O)Nc2ccn(CC(F)F)n2)Cc2ccccc21,2
+EOS57960,c1ccc(-c2nnc3ccc(N[C@H]4CCCC45OCCO5)nn23)cc1,2
+EOS58938,Cc1nn(C)c(C)c1CC(=O)N1CCC[C@H](n2ccnc2)C1,2
+EOS36643,CS(=O)(=O)N1CC(=O)N2CCc3ccccc3C2C1,2
+EOS32108,CCOCC(=O)N1CCC2(CC1)CN(c1ccc(C#N)c(C(F)(F)F)c1)CC2C(=O)NC,2
+EOS29164,CC(=O)Nc1ccc(-n2cnc(C(=O)N3CCN(c4cccc(C)c4C)CC3)c2)nc1,2
+EOS46827,Cc1ccc(CN(C)CC(=O)N(C)c2c(N)n(Cc3ccccc3)c(=O)[nH]c2=O)cc1,2
+EOS41610,CC(C)NC(=O)CN1CCCC[C@@H]1Cc1ccc(O)cc1,2
+EOS36335,Cn1c(=O)c(C(=O)NCc2cccnc2)c(O)c2ccccc21,2
+EOS921,Cc1nc(-c2ccc(N3CCN(C(=O)c4ccccc4)CC3)cc2)no1,2
+EOS102177,C[C@]12CCC3[C@@H](CC[C@]4(O)C[C@@H](O)CC[C@]34C=O)[C@@]1(O)CC[C@@H]2C1=CC(=O)OC1,2
+EOS26047,Cc1cccc(NC(=O)C2CCCN(S(=O)(=O)c3cn(C)cn3)C2)c1C,2
+EOS52350,Cc1ccc(S(=O)(=O)NCC[C@H]2CCNC2)c(C)c1.Cl,2
+EOS100819,CC1(COc2ccc3c(c2)ncn3-c2ccc3cccc(N4CCC(N)CC4)c3n2)COC1,2
+EOS37615,CN1C[C@@H](CCNC(=O)Nc2ccn3ccnc3c2)CC1=O,2
+EOS49443,NC(=O)c1cccc(Cc2noc([C@H]3CC34CCOCC4)n2)c1,2
+EOS77529,O=C(NCc1ccc(O)c(F)c1)Nc1ccc(-c2csnn2)cc1,2
+EOS87285,Cc1noc([C@@H]2COCCN2Cc2ccc(F)cc2F)n1,2
+EOS100247,CN1CCN(c2ccc(-c3ccncc3-c3cc(F)c(O)c(F)c3)cc2)CC1,2
+EOS9367,O=S(=O)(c1cccs1)N1CCc2[nH]cnc2C1c1cccnc1,2
+EOS59854,O=S1(=O)CCC(S(=O)(=O)N2CCC[C@H]2c2cccc3ccccc23)CC1,2
+EOS68838,Nc1nnc(SCC(=O)N2CCNC2=O)s1,2
+EOS25661,Cc1ccc(CNC(=O)CC(C)n2nc(C)cc2C)cc1,2
+EOS68480,Cc1cnn(C[C@H]2CCCCN2Cc2nccn2CC(F)(F)F)c1,2
+EOS86969,COc1ccccc1NC(=O)c1ccc(S(C)(=O)=O)cc1,2
+EOS53930,CC(=O)N1CCN([C@@H](C)C(=O)Nc2ccc(Cl)cc2)CC1,2
+EOS92311,COCC[C@@H](CO)NC(=O)c1csc(-c2cnc[nH]2)n1,2
+EOS75702,CC[C@H]1C(=O)NCCN1C(=O)c1cn(-c2ccc(Br)cc2)cn1,2
+EOS20463,CCn1nccc1C(=O)N1CCCC(N(Cc2cccnc2)C(=O)COC)C1,2
+EOS55812,Cc1nonc1CC(=O)N1CCCSC1,2
+EOS88541,Cc1cc(C)n(C[C@H]2CCCN2C(=O)c2c(C)noc2C)n1,2
+EOS96439,CCN1CC(=O)N([C@H]2CCCOc3cc(Cl)ccc32)C1=O,2
+EOS47481,Cc1ccc(N2C[C@@H](C(=O)N3CCC(C(=O)NCc4ccccc4)CC3)CC2=O)cc1F,2
+EOS2110,COCCn1c(SCCCc2ccccc2)nc2c1c(=O)[nH]c(=O)n2C,2
+EOS41657,CN1C(=O)N[C@@H](c2cccs2)C2=C1CN(CCO)C2=O,2
+EOS97500,C[C@H](NCCN1CCc2ccccc2C1=O)c1ccc(=O)[nH]n1,2
+EOS40840,CNC(=O)c1ccc(OCc2cc(F)cc3cccnc23)cc1,2
+EOS93595,Cc1cc(NC(=O)NC[C@H](C)N2CCOCC2)no1,2
+EOS26486,O=C(c1ccccc1)N1CCN(C(=O)c2cc(-c3ccncc3)[nH]n2)CC1,2
+EOS37008,O=C1NC2(CCCC2)C(=O)N1Cc1coc(-c2cccc(F)c2)n1,2
+EOS54478,COc1ccc(C(=O)Nc2ccc(NC(C)=O)cc2)cc1Br,2
+EOS23573,Cc1cc(NC(=O)c2cc(S(=O)(=O)N3CCOCC3)cn2C)ccc1Br,2
+EOS41714,CCN(C)C(=O)c1cccc(C(=O)Nc2cccc(-n3cccn3)c2)c1,2
+EOS42037,CC[C@H]1CN(C(=O)c2cn3c(n2)CCC3)CCN1CC(F)F,2
+EOS54525,COc1ccc(NC(C)=O)cc1Nc1ncnc2ccsc12,2
+EOS89481,Cc1cccc(S(=O)(=O)NC(=O)Cc2ccc(OCC#N)cc2)c1,2
+EOS60072,CCN(CC)C(=O)c1nn(-c2ccccc2)c(=O)c2ccccc12,2
+EOS21545,Cc1ccc(S(=O)(=O)Nc2ccc3c(c2)oc(=O)n3C)cc1,2
+EOS92249,Cc1ccnc(NC(=O)CN(C)CC(=O)N(C)Cc2ccco2)c1,2
+EOS91561,Cc1nc(CN2CCN(C(=O)Nc3cnn([C@@H](C)C4CC4)c3)CC2)oc1C,2
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/model_retrain/test_model_retrain.py b/atomsci/ddm/test/integrative/model_retrain/test_model_retrain.py
new file mode 100644
index 00000000..3063acc8
--- /dev/null
+++ b/atomsci/ddm/test/integrative/model_retrain/test_model_retrain.py
@@ -0,0 +1,86 @@
+import atomsci.ddm.pipeline.parameter_parser as parse
+import atomsci.ddm.pipeline.model_pipeline as mp
+import atomsci.ddm.pipeline.compare_models as cm
+import atomsci.ddm.utils.model_file_reader as mfr
+import atomsci.ddm.utils.test_utils as tu
+import atomsci.ddm.utils.model_retrain as mr
+import os
+import shutil
+import glob
+import json
+
+def clean(result_dir):
+    if os.path.exists(result_dir):
+        shutil.rmtree(result_dir)
+
+def train_model(result_dir):
+    """Train a model in production mode"""
+
+    json_file = tu.relative_to_file(__file__, './config.json')
+    example_file = tu.relative_to_file(__file__, './example.csv')
+
+    with open(json_file, 'r') as f:
+        config_json = json.load(f)
+        config_json['dataset_key'] = example_file
+        config_json['result_dir'] = result_dir
+
+    # Parse parameters
+    params = parse.wrapper(config_json)
+
+    # Create model pipeline
+    model = mp.ModelPipeline(params)
+
+    # Train model
+    model.train_model()
+
+def retrain_model(model_tar, new_result_dir, keep_seed):
+    """Retrains a model"""
+    mr.train_model_from_tar(model_tar, new_result_dir, keep_seed=keep_seed)
+
+def run_test_retrain(keep_seed):
+    """Trains and retrains a model
+    
+    Trains and retrains a model and compares the results
+    """
+
+    # train a model
+    result_dir = tu.relative_to_file(__file__, 'result')
+    train_model(result_dir)
+
+    # find the tar file
+    result_df = cm.get_filesystem_perf_results(result_dir)
+    assert(len(result_df) == 1)
+    model_tar = result_df['model_path'].values[0]
+
+    # retrain the model
+    new_result_dir = tu.relative_to_file(__file__, 'retrain_result')
+    retrain_model(model_tar, new_result_dir, keep_seed)
+
+    # find the new tar file
+    result_df = cm.get_filesystem_perf_results(new_result_dir)
+    assert(len(result_df) == 1)
+    new_model_tar = result_df['model_path'].values[0]
+
+    original_model = mfr.ModelFileReader(model_tar)
+    new_model = mfr.ModelFileReader(new_model_tar)
+
+    assert new_model.get_split_uuid() == original_model.get_split_uuid()
+
+    if keep_seed:
+        assert new_model.get_random_seed()==original_model.get_random_seed()
+    else:
+        assert new_model.get_random_seed()!=original_model.get_random_seed()
+
+    # clean files
+    split_files = glob.glob(tu.relative_to_file(__file__, './example_*_random_*.csv'))
+    for sf in split_files:
+        os.remove(sf)
+    clean(new_result_dir)
+    clean(result_dir)
+
+def test_retrain():
+    run_test_retrain(True)
+    run_test_retrain(False)
+
+if __name__ == '__main__':
+    test_retrain()
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/multitask_split/test_split.py b/atomsci/ddm/test/integrative/multitask_split/test_split.py
index d47ecc3b..f89d6fdb 100644
--- a/atomsci/ddm/test/integrative/multitask_split/test_split.py
+++ b/atomsci/ddm/test/integrative/multitask_split/test_split.py
@@ -41,6 +41,54 @@ def clean():
     delete_file('thirty_gen_split.csv')
     delete_file('ss_split.csv')
 
+def test_seeded_splits():
+    clean()
+
+    init_data()
+
+    smiles_col = 'compound_id'
+    id_col = 'compound_id'
+    frac_train = 0.8
+    frac_test = 0.1
+    frac_valid = 0.1
+    num_super_scaffolds = 60
+    dfw = 2 # chemical distance importance weight
+    rfw = 1 # split fraction importance weight
+
+    total_df = pd.read_csv('KCNA5_KCNH2_SCN5A_data.csv', dtype={id_col:str})
+    response_cols = ['target_KCNA5_standard_value',
+        'target_KCNH2_standard_value',
+        'target_SCN5A_activity']
+
+    # -------------------------------------------------------------------------
+    # one generation multitask scaffold split
+    mss = MultitaskScaffoldSplitter()
+    A_split_df = split_with(total_df, mss, 
+        smiles_col=smiles_col, id_col=id_col, response_cols=response_cols, 
+        diff_fitness_weight_tvt=dfw, ratio_fitness_weight=rfw, num_generations=1,
+        num_super_scaffolds=num_super_scaffolds,
+        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid, seed=0)
+
+    b_mss = MultitaskScaffoldSplitter()
+    B_split_df = split_with(total_df, b_mss, 
+        smiles_col=smiles_col, id_col=id_col, response_cols=response_cols, 
+        diff_fitness_weight_tvt=dfw, ratio_fitness_weight=rfw, num_generations=1,
+        num_super_scaffolds=num_super_scaffolds,
+        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid, seed=0)
+
+    c_mss = MultitaskScaffoldSplitter()
+    C_split_df = split_with(total_df, c_mss, 
+        smiles_col=smiles_col, id_col=id_col, response_cols=response_cols, 
+        diff_fitness_weight_tvt=dfw, ratio_fitness_weight=rfw, num_generations=1,
+        num_super_scaffolds=num_super_scaffolds,
+        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid, seed=42)
+
+    assert all(A_split_df['cmpd_id']==B_split_df['cmpd_id']) and all(A_split_df['subset']==B_split_df['subset'])
+    # compounds can be in the same order
+    assert not all(A_split_df['subset']==C_split_df['subset'])
+
+    clean()
+
 def test_splits():
     clean()
 
@@ -69,7 +117,7 @@ def test_splits():
         smiles_col=smiles_col, id_col=id_col, response_cols=response_cols, 
         diff_fitness_weight_tvt=dfw, ratio_fitness_weight=rfw, num_generations=1,
         num_super_scaffolds=num_super_scaffolds,
-        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid)
+        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid, seed=0)
     mss_split_df.to_csv('one_gen_split.csv', index=False)
     assert len(total_df) == len(mss_split_df)
 
@@ -86,7 +134,7 @@ def test_splits():
         diff_fitness_weight_tvt=dfw, ratio_fitness_weight=rfw,
         num_generations=num_generations,
         num_super_scaffolds=num_super_scaffolds,
-        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid)
+        frac_train=frac_train, frac_test=frac_test, frac_valid=frac_valid, seed=0)
     mss_split_df.to_csv('thirty_gen_split.csv', index=False)
     assert len(total_df) == len(mss_split_df)
 
@@ -166,6 +214,7 @@ def test_pipeline_split_and_train():
     clean()
 
 if __name__ == '__main__':
-    test_splits()
+    test_seeded_splits()
+    #test_splits()
     #test_pipeline_split_only()
     #test_pipeline_split_and_train()
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/nanobret_multitask_classification_data.csv b/atomsci/ddm/test/integrative/sampling_test/nanobret_multitask_classification_data.csv
new file mode 100755
index 00000000..f2d8d242
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/nanobret_multitask_classification_data.csv
@@ -0,0 +1,431 @@
+,compound_id,base_rdkit_smiles,NEK1_relation,NEK1_mean_pIC50,NEK11_relation,NEK11_mean_pIC50,NEK2_relation,NEK2_mean_pIC50,NEK3_relation,NEK3_mean_pIC50,NEK4_relation,NEK4_mean_pIC50,NEK5_relation,NEK5_mean_pIC50,NEK6_relation,NEK6_mean_pIC50,NEK9_relation,NEK9_mean_pIC50,NEK1_active,NEK11_active,NEK2_active,NEK3_active,NEK4_active,NEK5_active,NEK6_active,NEK9_active
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+1,PAR_315,CCN(CC)S(=O)(=O)c1cc(Nc2nccc(-c3ccc(C(=O)NCC#N)cc3)n2)ccc1Cl,,5.552056004461681,,5.4574860448455205,,5.238326759769438,,5.430757234170256,,5.445163827978223,,5.486603647930933,<,4.522878745280337,,5.252417203511357,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+5,Narazaciclib,CN1CCN(c2ccc(Nc3ncc4cc(C#N)c(=O)n(C5CCCC5)c4n3)cc2)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+6,HO_N_57_1,COc1ccc(-c2cc3nccn3c(Nc3ccccc3C#N)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+7,BA_03_61_01,COc1cc(Nc2ncc3c(C)cc(=O)n(C4CCOCC4)c3n2)cc(OC)c1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+8,PAR_363,N#CCNC(=O)c1ccc(-c2ccnc(Nc3cccc(CN4CCOCC4)c3)n2)cc1,,6.151159915288579,,4.583360913493843,,4.832123723463034,,6.1451321031000266,,4.635987721962014,,6.851435811405105,<,4.522878745280337,,5.269015756005547,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0
+9,PAR_182,CN1CCN(c2ccc(Nc3nccc(-c4ccc(C(=O)NCC#N)cc4)n3)cc2F)CC1,,5.509596275828427,<,4.522878745280337,,4.835439584676596,,5.8385226992247405,<,4.522878745280337,,6.49132807858564,<,4.522878745280337,,5.156335184776021,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+10,PAR_335,C#Cc1cc(Nc2nccc(-c3ccc(NC(=O)C4CCCN4)cc3)n2)ccc1Cl,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.237059415948903,<,4.522878745280337,,5.254416153865931,<,4.522878745280337,,4.802579991224726,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+11,BA_03_69_c,COc1cc2ncnc(-c3ccc(C(N)=O)c(F)c3)c2cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+12,PAR_334,O=C(Nc1ccc(-c2ccnc(Nc3cccc(N4CCOCC4)c3)n2)cc1)C1CCCN1,,5.027331341083788,<,4.522878745280337,<,4.522878745280337,,6.230662437350819,<,4.522878745280337,,5.892418801404399,<,4.522878745280337,,5.308137455897522,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+13,ZDG_7_50_2,CN1CCN(c2ccc(Nc3nccc(-c4ccc(NC(=O)[C@@H]5CCCN5)cc4)n3)cc2F)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+14,HO_N_135_4_A,COc1ccc(-c2cc3nccn3c(Nc3ncccc3C(N)=O)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+15,3746,N#Cc1c(Nc2nc(Nc3ccc(N4CCNCC4)cc3)ncc2Cl)cccc1OCc1c(F)cccc1F,,,,,,7.30102999566398,,,,,,,,,,,,,1.0,,,,,
+16,3827,CCOc1ccc2c(-c3ccnc(Nc4cccc(Br)c4)n3)cnn2n1,,,,,,,,,,,,6.752026733638193,,,,,,,,,,1.0,,
+17,Altiratinib,O=C(Nc1cc(Oc2cc(F)c(NC(=O)C3(C(=O)Nc4ccc(F)cc4)CC3)cc2F)ccn1)C1CC1,,5.250812839247721,,5.092031771170105,,5.0900326887847855,,5.022491146605316,,5.346348290654262,<,4.522878745280337,<,4.522878745280337,,4.975134466817432,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+18,PAR_252,O=C(Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)c1cccc(-c2cccnc2)c1,,5.0542453284308175,,5.100565607974711,,4.900463929543359,,5.012345426898412,,4.460761357368833,,4.980072153113726,<,4.522878745280337,,4.766580186299862,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+19,ATH686,CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4ccnc(N)n4)cc3)cc2C(F)(F)F)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.681749789015572,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+20,TL01_022,COc1ccc(Nc2nccc(-c3ccc(S(=O)(=O)NCCN)cc3)n2)cc1,,5.712814683943481,<,4.522878745280337,,4.6322874985094575,,4.978363242436672,<,4.522878745280337,,6.341054794006962,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+21,ZDG_7_43_3,Cn1cc(-c2ccc(N3CCOCC3)cc2)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+22,BA_03_53_12,COc1ccc2c(-c3ccnc(Nc4cccc(N5CCOCC5)c4)n3)cnn2n1,,6.062751461067501,,5.118481547126134,,5.487245060747732,,6.373063665619277,,5.033558942245732,<,4.522878745280337,<,4.522878745280337,,6.705887594577249,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0
+23,PAR_379,CN(C)CCCNC(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.05937779651617,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+24,PAR_376,O=C(CC12CC3CC(CC(C3)C1)C2)Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,4.953028453525107,,5.006589807434891,,4.846953375389458,,5.046613355088627,,4.981795846064236,,5.115687643819264,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+25,PAR_375,Cc1cccc(C(=O)Nc2ccc(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cc2)n1,,5.051954664361819,,5.048577381602782,,4.824723451097711,,4.943373766429193,,4.945726828962131,,5.188651826933941,<,4.522878745280337,,4.915625881147178,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+26,PAR_331,O=C(Nc1ccc(-c2ccnc(Nc3cc(N4CCOCC4)cc(C(F)(F)F)c3)n2)cc1)C1CCCN1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.738666862982437,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+27,BA_03_50_04,COc1ccc2ncnc(Nc3cccc(S(N)(=O)=O)c3)c2c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+28,PAR_403,COc1ccccc1CC(=O)N[C@H]1CCN(c2ccnc(Nc3cc(OC)c(OC)c(OC)c3)n2)C1,,4.622548972536967,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+29,GSK329,CNc1cc(Oc2c(Cl)cc(NC(=O)Nc3cccc(C(F)(F)F)c3)cc2Cl)ncn1,<,4.522878745280337,,5.1792726384345125,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+30,PAR_225,N#CCNC(=O)c1ccc(-c2ccnc(NCCn3ccnc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+31,PAR_380,O=C(Nc1cc(O)ccn1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,4.920029645072777,<,4.522878745280337,<,4.522878745280337,,6.095333598804788,<,4.522878745280337,,6.511594894580872,<,4.522878745280337,,5.183946010215339,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0
+32,ZDG_7_46_3,Cn1cc(-c2cc3ccccc3o2)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+33,HO_N_138_3_A,Cn1ncc2cc(-c3cc4nccn4c(Nc4ncccc4C(N)=O)n3)ccc21,,4.55056495781959,<,4.522878745280337,<,4.522878745280337,,4.672852955355713,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+34,HO_N_90,COc1ccc(-c2cc3nccn3c(Nc3cc4ccccc4cn3)n2)cc1OC,,4.764212049256466,,4.67578301752895,,4.644063314968692,<,4.522878745280337,,4.765623989645558,<,4.522878745280337,<,4.522878745280337,,4.90264845805378,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+35,PAR_142,O=C(NCCC1CCOCC1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.6308182614698215,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+36,BA_03_56_12,CCOc1ccc2c(-c3ccnc(Nc4cccc(N5CCOCC5)c4)n3)cnn2n1,,6.02374236875252,,5.056969533906477,,5.365743632392702,,6.281789923080026,,4.976667870676705,,7.744195610646878,<,4.522878745280337,,6.463483079487036,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0
+37,TL01_026,COc1ccccc1Nc1nccc(-c2ccc(S(=O)(=O)NCCN)cc2)n1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+38,GW843682,COc1cc2ncn(-c3cc(OCc4ccccc4C(F)(F)F)c(C(N)=O)s3)c2cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+39,BA_03_60_04,CCCCn1c(=O)cc(C)c2cnc(Nc3ccc(N4CCN(C)CC4)c(F)c3)nc21,,4.561733678166957,<,4.522878745280337,,5.711470552571224,<,4.522878745280337,,4.637347915145522,,5.159545510921731,<,4.522878745280337,,5.401638890483255,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+40,HO_N_135_2_A,NC(=O)c1cccnc1Nc1nc(-c2ccc3c(c2)OCCO3)cc2nccn12,<,4.522878745280337,,4.986066288377341,,5.090364874929746,,5.502197651941389,,5.08345638049453,,5.49749941815019,<,4.522878745280337,,5.4066312032053725,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+41,AST487,CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+42,PAR_310,N#CCNC(=O)c1ccc(-c2ccnc(Nc3ccc(Cl)cc3)n2)cc1,,5.458554470785787,,5.096843984530558,,4.728376307122485,,5.254035848504938,<,4.522878745280337,,6.376312835930973,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+43,PAR_268,CC(C)(C)CNC(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,5.890530010199987,,4.985652618950243,,4.924572396157946,,5.500165740249142,<,4.522878745280337,,6.4941348376070565,<,4.522878745280337,,5.183988608659089,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+44,HO_N_133_2_A,NC(=O)c1cccnc1Nc1nc(-c2ccnc(F)c2)cc2nccn12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+45,BA_03_53_01,COc1ccc2c(-c3ccnc(Nc4cc(OC)c(OC)c(OC)c4)n3)cnn2n1,,5.773880703765544,,5.112542418995005,,5.374874677070926,,5.551742176902253,,4.927485323089146,,7.174881717215393,<,4.522878745280337,,6.891121194058334,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0
+46,TL01_019,O=S(=O)(NCCNS(=O)(=O)C1CC1)c1ccc(-c2ccnc(Nc3ccccc3)n2)cc1,,5.6302895225802505,,5.015573097083401,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,6.447615587988817,<,4.522878745280337,,4.991791916486836,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+47,PAR_291,COc1cc(Nc2nccc(-c3ccc(C(=O)NCC#N)cc3)n2)ccc1N1CCOCC1,,4.653285401787367,<,4.522878745280337,<,4.522878745280337,,4.997500809031838,<,4.522878745280337,,5.326959152242042,<,4.522878745280337,,4.894680040351775,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+48,ZDG_7_40_C,Cc1ccccc1CCC(=O)N1CCc2cc(-c3cn(C)c4ncnc(N)c34)ccc21,,5.200969778775301,,4.776809615678486,,5.775092389602924,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+49,ZDG_6_64,Cn1nc(-c2cccc(NS(C)(=O)=O)c2)c2nc(Nc3ccc(Cl)cc3)ncc21,,5.424252817681134,,5.174190495793877,,5.275611497701943,,5.367483415538971,,5.211276375328347,<,4.522878745280337,<,4.522878745280337,,5.421185973017695,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+50,HO_N_136_6_A,Cn1nccc1-c1cc2nccn2c(Nc2ncccc2C(N)=O)n1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+51,BA_03_59_02,Cc1cc(=O)n(C2CCC2)c2nc(Nc3cccc(N4CCOCC4)c3)ncc12,,5.001601822254985,,4.693183054753221,,6.373729650814312,<,4.522878745280337,<,4.522878745280337,,6.082990515782506,<,4.522878745280337,,5.437486302264579,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0
+52,BA_03_78_d,Clc1ccc(CCNc2ncnc3sc(Br)cc23)cc1,<,4.522878745280337,,5.350427583242821,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+53,ZDG_7_44_1,Cn1cc(C2=CCOCC2)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+54,PAR_294,CN1CCN(c2ccc(Nc3nccc(-c4ccc(C(=O)NCC#N)cc4)n3)cc2)CC1,,5.044635900449657,<,4.522878745280337,,4.560321091507077,,5.273291252702858,<,4.522878745280337,,5.827809393613137,<,4.522878745280337,,4.842322140279872,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+55,BBT594,CC(=O)Nc1cc(Oc2ccc3c(c2)CCN3C(=O)Nc2ccc(CN3CCN(C)CC3)c(C(F)(F)F)c2)ncn1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.799164165543371,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+56,PAR_298,CN1CCN(C(=O)c2ccc(Nc3nccc(-c4ccc(C(=O)NCC#N)cc4)n3)cc2)CC1,,5.251145281525807,<,4.522878745280337,<,4.522878745280337,,5.0225671244809424,<,4.522878745280337,,5.613491143015396,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+57,PAR_269,O=C(NCC1CCC1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,4.994533139752917,,4.927268579769375,,4.883063139568752,,5.352743745097549,,4.959082244697873,,5.608610180689029,<,4.522878745280337,,5.144686349387098,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+58,PAR_404,COc1cc(Nc2nccc(N3CC[C@H](NC(=O)c4ccc(F)cc4F)C3)n2)cc(OC)c1OC,,5.558465684840074,,4.541361905337933,,4.900332026351334,<,4.522878745280337,,4.739977521082981,,5.572057466455448,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+59,EF_3_203,CS(=O)(=O)c1cccc(Nc2nccc(N3CC[C@H](NC(=O)COc4ccccc4)C3)n2)c1,,4.784048510365078,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.0071295739106665,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+60,HO_N_96,CN1CCN(c2ccc(Nc3ncc4c(n3)c(-c3cccc(NS(C)(=O)=O)c3)nn4C)nc2)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.408824154707516,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+61,ZDG_7_51_5,Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(C(=O)N4CCN(C)CC4)cc3)ncc12,,4.90796498375648,<,4.522878745280337,,5.137433522832007,<,4.522878745280337,,4.62633228335401,,5.742267504842507,<,4.522878745280337,,5.141072538859764,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+62,TL01_020,NCCNS(=O)(=O)c1ccc(-c2ccnc(NC3CCNCC3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+63,PAR_261,CC1CC1C(=O)Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,5.354036283168988,,4.914258788385221,,4.810569214288567,,5.059421625497709,,4.602053372163663,,5.929412530692798,<,4.522878745280337,,4.926643299867915,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+64,HO_N_42,COc1ccc(-c2cc3nccn3c(Nc3ccccc3C(N)=O)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+65,BA_03_50_15,COc1ccc2ncnc(NCCc3cccc(F)c3)c2c1,<,4.522878745280337,,5.138509873236028,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+66,PAR_158,CC(C#N)NC(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,5.7386084272523705,,4.701338559819312,,5.121475239081094,,6.005153475423644,,4.729687113950752,,6.675994355441816,<,4.522878745280337,,5.366898760053524,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0
+67,PAR_355,Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccn(C(F)F)n3)ncc12,,4.600182760304779,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.357145720986832,<,4.522878745280337,,4.694706720393334,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+68,GSK2606414,Cn1cc(-c2ccc3c(c2)CCN3C(=O)Cc2cccc(C(F)(F)F)c2)c2c(N)ncnc21,,4.750939925719619,,5.224233686651976,,5.383612695573137,<,4.522878745280337,,4.256337912912628,,4.674469636983738,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+69,PAR_402,CNc1ccc(C(=O)N[C@H]2CCN(c3ccnc(Nc4cc(OC)c(OC)c(OC)c4)n3)C2)cc1,,5.3976646548335685,,5.152360176139837,,5.100609381453203,<,4.522878745280337,,5.219988218415373,,5.787184715248259,<,4.522878745280337,,4.920008110197255,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+70,3820,CCOc1ccc2c(-c3ccnc(Nc4ccc(N5CCN(C)CC5)c(F)c4)n3)cnn2n1,,,,,,,,,,,,7.327902142064281,,,,,,,,,,1.0,,
+71,Tovorafenib,C[C@@H](NC(=O)c1ncnc(N)c1Cl)c1ncc(C(=O)Nc2cc(C(F)(F)F)c(Cl)cn2)s1,<,4.522878745280337,,4.768657012738542,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+72,ZDG_6_75_4,COc1cc(Nc2ncc3c(n2)c(-c2ccc(F)c(F)c2)nn3C)cc(OC)c1OC,,6.084243400188249,<,4.522878745280337,,5.6204374173285006,,5.94641893903062,<,4.522878745280337,,7.6506234286994035,<,4.522878745280337,,5.529835856645433,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+73,BA_03_50_18,COc1ccc2ncnc(NCCc3cccc(Br)c3)c2c1,<,4.522878745280337,,5.173425534719117,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+74,PAR_323,Cc1ccc(Nc2nccc(-c3ccc(NC(=O)C4CCCN4)cc3)n2)cc1O,,4.835689359282434,,4.573961326928361,<,4.522878745280337,,5.734303057224471,<,4.522878745280337,,5.839903129257649,<,4.522878745280337,,4.7368123982363,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+75,PAR_112,CC(C)(CN)CNC(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+76,HO_N_105,COc1ccc(-c2cc3nccn3c(Nc3ccc(N4CCN(C)CC4)cc3)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+77,BA_03_56_11,CCOc1ccc2c(-c3ccnc(Nc4cc(N5CCOCC5)cc(C(F)(F)F)c4)n3)cnn2n1,,4.87182347956317,<,4.522878745280337,,4.742710778519723,,4.791763951581726,<,4.522878745280337,,6.229989330291368,<,4.522878745280337,,6.356460014325777,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0
+78,GCN2_IN_1,c1nn(C2CCOCC2)cc1Nc1ncc2nnn(-c3ccc4cn[nH]c4c3)c2n1,,4.73444963419206,<,4.522878745280337,,5.686990572523224,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+79,BA_03_61_07,COc1ccccc1Nc1ncc2c(C)cc(=O)n(C3CCOCC3)c2n1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+80,PAR_314,Cc1cc(Nc2nccc(-c3ccc(C(=O)NCC#N)cc3)n2)ccc1N1CCOCC1,,5.400563622013164,,4.642276208868983,,4.830992345816636,,5.410659988231124,<,4.522878745280337,,6.183031258097228,<,4.522878745280337,,5.102286733139374,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+81,PAR_157,N#CNC(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,4.608026971547095,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+82,PAR_109,CC(C)N(C(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)C(C)C,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.74539665738371,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+83,ZDG_7_39_A,Cn1cc(-c2ccc3c(c2)CCN3C(=O)Cc2ccc(C(C)(C)C)cc2)c2c(N)ncnc21,,4.715600709998829,,5.687502309749495,,6.283088612988116,<,4.522878745280337,,5.209563989701855,,4.666105933885059,<,4.522878745280337,<,4.522878745280337,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+84,PAR_406,COc1cc(Nc2nccc(N3CC[C@H](NC(=O)C4CCN(C)CC4)C3)n2)cc(OC)c1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+85,BA_03_55_01,COc1cc(Nc2nccc(-c3cnn4ncccc34)n2)cc(OC)c1OC,,5.132987567403431,<,4.522878745280337,,4.856408882448614,<,4.522878745280337,<,4.522878745280337,,5.939016896221744,<,4.522878745280337,,5.883734193517436,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+86,Axitinib,CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1,<,4.522878745280337,,4.867104511077254,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+87,BA_03_50_05,COc1ccc2ncnc(Nc3c(F)cccc3F)c2c1,<,4.522878745280337,,4.596618862743608,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+88,3829,COc1cccc(Nc2nccc(-c3cnn4nc(OC)ccc34)n2)c1,,5.255655162286165,,4.846115892045417,,4.900122251889135,,5.610499231954557,,4.715548992582638,,6.705214911312019,<,4.522878745280337,,6.072790227115989,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0
+89,ZDG_7_31_A,Cn1cc(-c2ccc3c(c2)CCN3C(=O)Cc2cccc(Cl)c2)c2c(N)ncnc21,,5.452325606109792,,5.400397619401221,,5.831193316226468,<,4.522878745280337,<,4.522878745280337,,5.060020330334237,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+90,ZDG_2_91,CCn1nc(-c2cccc(NS(C)(=O)=O)c2)c2nc(Nc3cc(OC)c(OC)c(OC)c3)ncc21,<,4.522878745280337,,5.363141976659056,,6.800229265996677,,5.580865079060308,,5.392522529985525,,6.964068228651505,<,4.522878745280337,,6.9750003957975135,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0
+91,PAR_159,N#CCCNC(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,5.553248711844009,<,4.522878745280337,,4.720220027609966,,5.556286074241282,<,4.522878745280337,,5.991463349980734,<,4.522878745280337,,5.309241281674459,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+92,HO_N_133_4_A,NC(=O)c1cccnc1Nc1nc(-c2ccc(S(=O)(=O)N3CCCC3)cc2)cc2nccn12,,5.167744975172301,,5.0077681177720725,<,4.522878745280337,,4.627196303033837,<,4.522878745280337,,4.887691096286184,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+93,2093,CN(C)C/C=C/C(=O)Nc1cccc(C(=O)Nc2ccc(Nc3nccc(-c4cccnc4)n3)cc2F)c1,,,,,,,,,,,,5.6925039620867866,,,,,,,,,,0.0,,
+94,PAR_405,COc1cc(Nc2nccc(N3CC[C@H](NC(=O)CC45CC6CC(CC(C6)C4)C5)C3)n2)cc(OC)c1OC,,4.981411343474717,,4.761225157557821,,4.72133828277282,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+95,HO_N_67,COc1ccc(-c2cc3nccn3c(Nc3ccccc3NS(C)(=O)=O)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+96,HO_N_136_5_A,NC(=O)c1cccnc1Nc1nc(-c2ccccc2F)cc2nccn12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.766690674784758,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.600551282564767,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+97,HO_N_57_2,COc1ccc(-c2cc3nccn3c(Nc3cccnc3C(N)=O)n2)cc1OC,,5.23997849830724,,4.969907720086619,,5.191222602450658,<,4.522878745280337,,5.242906129665422,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+98,PAR_320,CC(C)c1ccc(Nc2nccc(-c3ccc(C(=O)NCC#N)cc3)n2)cc1,,5.543472992608432,,4.928232072657166,<,4.522878745280337,,5.411014810538559,,4.755247751878081,,6.353178256283163,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+99,Encorafenib,COC(=O)N[C@@H](C)CNc1nccc(-c2cn(C(C)C)nc2-c2cc(Cl)cc(NS(C)(=O)=O)c2F)n1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+100,ZDG_6_51_2,COc1cc(Nc2ncc3c(n2)c(-c2cccc(NC(=O)CC#N)c2)nn3C)cc(OC)c1OC,,6.52944874090885,,5.696549972826222,,6.530886968451726,,6.357595202448904,,5.638167199966386,,8.103824589968996,<,4.522878745280337,,7.184540852844173,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0
+101,BA_03_56_14,CCCCNC(=O)c1cc(F)cc(Nc2nccc(-c3cnn4nc(OCC)ccc34)n2)c1,,5.189636537748015,<,4.522878745280337,,5.204904108573477,,5.659405529983294,,5.19138944283402,,6.214638971935892,<,4.522878745280337,,5.645197983557392,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+102,3819,CCOc1ccc2c(-c3ccnc(Nc4ccc(OC)c(OC)c4)n3)cnn2n1,,,,,,,,,,,,7.537602002101043,,,,,,,,,,1.0,,
+103,Rac_CCT_250863,CC(/C=C\C(F)(F)F)Oc1cc(-c2cc(-c3cc(CN(C)C)cs3)cnc2N)ccc1C(N)=O,<,4.522878745280337,<,4.522878745280337,,5.471954824304111,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+104,PAR_330,CS(=O)(=O)c1cccc(Nc2nccc(-c3ccc(NC(=O)C4CCCN4)cc3)n2)c1,,4.944417751456352,,4.548317198621807,<,4.522878745280337,,5.730813648408817,<,4.522878745280337,,5.480188371406996,<,4.522878745280337,,5.378530517324077,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+105,PAR_377,CC1(C)CC1C(=O)Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,6.105174967139251,,5.753508816051484,,5.623341311836982,,5.80430564114047,,5.432611278347096,,6.792188865778642,<,4.522878745280337,<,4.522878745280337,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+106,PAR_383,CCCc1ccnc(NC(=O)c2ccc(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cc2)c1,,5.29199862846524,,5.3124428431629465,,5.135043190488083,,5.174283523278509,,5.239410092259872,,5.375056794357965,<,4.522878745280337,,5.022852102453122,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+114,PAR_369,CC1(C(=O)Nc2ccc(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cc2)CCCC1,,6.127525053826286,,5.784071210080595,,5.50639449696777,,6.218905861112133,,4.771642567171203,,7.190435850083502,<,4.522878745280337,,6.041095247784531,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0
+115,ZDG_6_49_1,COc1cc(Nc2ncc3c(-c4cccc(NCC(F)(F)F)c4)nn(C)c3n2)cc(OC)c1OC,,6.691293263430712,,5.284512969713438,,6.212753276097712,,6.966896451516143,,5.369403937201246,<,4.522878745280337,<,4.522878745280337,,6.663831731885772,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0
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+117,ZDG_7_33_C,Cn1cc(-c2ccc3c(c2)CCN3C(=O)Cc2cscn2)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+120,BA_03_53_11,COc1ccc2c(-c3ccnc(Nc4cc(N5CCOCC5)cc(C(F)(F)F)c4)n3)cnn2n1,,5.113626204819444,,4.769725578487549,,5.080090690342586,,5.1028409189818,,4.804196269857379,,6.217860891122144,<,4.522878745280337,,6.7256421310134185,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0
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+130,PAR_249,O=C(Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)C1CCC(O)CC1,,5.007421559397016,<,4.522878745280337,<,4.522878745280337,,5.55859615288902,<,4.522878745280337,,5.8989347544814414,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+138,ZDG_6_50_1,COc1cc(Nc2ncc3c(-c4cccc(NC(=O)N5CCCC5)c4)nn(C)c3n2)cc(OC)c1OC,,6.0959209900587465,,5.649537330654971,,6.00111694950526,,5.103189913336463,,5.355920820754059,,7.112201749932475,<,4.522878745280337,,5.987651242211193,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0
+139,ZDG_7_52_6,CN1CCN(c2cccc(Nc3nccc(-c4ccc(C(=O)NCC#N)cc4)n3)c2)CC1,,5.896288707022026,,4.591986977837131,,5.022729260872255,,5.875633568854028,,4.567101299453912,,6.736106345680188,<,4.522878745280337,,5.48560900469894,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
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+182,ZDG_7_37_B,Cc1nn(C)c(C)c1CC(=O)N1CCc2cc(-c3cn(C)c4ncnc(N)c34)ccc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+184,ZDG_7_11,COCCN(C)C(=O)c1ccc(Nc2ncc3c(C)cc(=O)n(C4CCCC4)c3n2)cc1,,5.038417673220524,<,4.522878745280337,,5.139779690319417,<,4.522878745280337,,4.819320265352316,,5.575840996623945,<,4.522878745280337,,5.1470809063912935,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+185,ZDG_7_43_2,Cn1cc(-c2ccc3c(c2)CC(=O)N3)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+186,PAR_205,Cc1cc(C(=O)N[C@H]2CCN(C(=O)Nc3nc4c(s3)-c3nc(-c5ccccc5Br)ncc3CC4)C2)nn1C,,4.706292274445287,<,4.522878745280337,<,4.522878745280337,,5.1667083090804,<,4.522878745280337,,5.350574849239629,<,4.522878745280337,,4.7491437507531495,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+202,PAR_90,O=C(NCc1cccc(F)c1)Nc1nc2c(s1)-c1nc(-c3ccccc3Br)ncc1CC2,,5.077431289362347,,4.914928066065915,,4.694492166193196,,5.061981637898671,,4.988231387689668,,5.545137437022733,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+203,BA_03_60_01,CCCCn1c(=O)cc(C)c2cnc(Nc3cc(OC)c(OC)c(OC)c3)nc21,,5.659723155021703,,4.689374220300698,,5.7300472567178415,,5.339274626147458,,4.907717682813136,,6.357928805442624,<,4.522878745280337,,5.845826566736619,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+204,BA_03_59_05,Cc1cc(=O)n(C2CCC2)c2nc(Nc3cccc(S(C)(=O)=O)c3)ncc12,,5.042704399864713,,4.780353647381379,,5.583684563735657,<,4.522878745280337,,4.85857669611246,,5.476412266461561,<,4.522878745280337,,5.600960957136003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+205,PAR_338,Cc1ccccc1CCC(=O)Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,5.773842492893935,,5.663966776523732,,5.606180250657419,,5.8085663054789824,,5.740628400795056,,6.166726880865653,<,4.522878745280337,,5.5277440733905285,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+206,ZDG_7_44_4,COc1ncccc1-c1cn(C)c2ncnc(N)c12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+207,ZDG_7_43_4,COc1ccc(-c2cn(C)c3ncnc(N)c23)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+208,BA_03_59_07,COc1ccccc1Nc1ncc2c(C)cc(=O)n(C3CCC3)c2n1,,4.627146505382027,,4.53833768508282,,4.719127822874644,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+209,ZDG_6_48_4,COc1cc(Nc2ncc3c(-c4cccc(NS(=O)(=O)C(F)(F)F)c4)nn(C)c3n2)cc(OC)c1OC,,6.7362783823558665,,5.660878745330506,,7.070391042503128,,7.128762995687709,,4.933226575990378,<,4.522878745280337,<,4.522878745280337,,7.200467705751264,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0
+210,BA_03_65_c,COC(=O)c1sc(-n2cnc3cc(OC)c(OC)cc32)cc1OCc1sccc1F,,5.074721428638421,,4.965014934450227,,5.219896358959755,<,4.522878745280337,,4.849654758439626,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+211,HO_N_132_3_E,COC(=O)c1cccnc1Nc1nc(-c2ccc(OC)cc2)cc2nccn12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+212,BA_03_61_04,Cc1cc(=O)n(C2CCOCC2)c2nc(Nc3ccc(N4CCN(C)CC4)c(F)c3)ncc12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.9873329366252745,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+213,3853,COc1ccc2c(-c3ccnc(Nc4cccc(C(F)(F)F)c4)n3)cnn2n1,,,,,,,,,,,,5.608888386297197,,,,,,,,,,0.0,,
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+215,HO_N_133_1_A,NC(=O)c1cccnc1Nc1nc(C2=CCOCC2)cc2nccn12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.7820580655384335,<,4.522878745280337,,4.669912426300299,<,4.522878745280337,,4.574860731113751,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+216,PAR_360,N#CCNC(=O)c1ccc(-c2ccnc(Nc3ccc(CN4CCOCC4)cc3)n2)cc1,,5.396623625558428,,4.7179591764319255,,4.891123299789348,,5.435824480800892,,4.818275633982171,,5.611671019139783,<,4.522878745280337,,5.15556164378403,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+218,BA_03_80_A,Cc1cc(=O)n(C2CCOCC2)c2nc(Nc3ccc(N4CCN(C)CC4)cn3)ncc12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+221,BA_03_66_h,COc1cc2ncn(-c3cc(OCc4ccn(C)n4)c(C(N)=O)s3)c2cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+222,BA_03_60_10,CCCCn1c(=O)cc(C)c2cnc(Nc3ccc(C(=O)NC(C)C)cc3)nc21,,4.678728850443628,<,4.522878745280337,,5.1861188093075405,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.896264415193177,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+223,TC_S_7005,C[C@H](Nc1cc2c(-c3ccc4c(c3)OCO4)noc2cn1)c1ccccc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.83095972316797,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.816293228943018,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+224,ZDG_7_24_1,COc1cc2ncn(-c3cc(OCc4cccc(F)c4)c(C(N)=O)s3)c2cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+226,PAR_400,COc1ccc(CNc2nccc(N3CC[C@H](NC(=O)COc4ccccc4)C3)n2)cc1Cl,<,4.522878745280337,,4.651760068086324,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+227,RSS0680,Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)CCCCCN2CCN(c3ccc(Nc4ncc5scc(-c6cccc(NS(C)(=O)=O)c6)c5n4)cc3)CC2)C(C)(C)C)cc1,,6.54261166447481,,5.75585800388447,,6.295388017169884,,5.999097187127363,,5.848763313386655,,6.030727908922796,<,4.522878745280337,,6.611177726392288,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0
+228,ALK_kinase_inhibitor_1,COc1ccc(F)cc1-c1c(CO)sc2cnc(Nc3ccc(N4CCN(C)CC4)cc3OC(C)C)nc12,<,4.522878745280337,<,4.522878745280337,,4.536829647512487,<,4.522878745280337,,4.543123388688776,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+230,BA_03_60_02,CCCCn1c(=O)cc(C)c2cnc(Nc3cccc(N4CCOCC4)c3)nc21,,5.343366504225447,,4.983140207370476,,5.92190220515168,,5.045371664249361,,4.993732625917914,,6.195057877289041,<,4.522878745280337,,6.116489048104294,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0
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+232,ZDG_6_75_2,COc1cc(Nc2ncc3c(n2)c(-c2ccccc2NC(C)=O)nn3C)cc(OC)c1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.8419011094092586,<,4.522878745280337,,4.682799614428752,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+233,EF_3_105,COc1cc(Nc2nccc(N3CC[C@H](NC(=O)Cc4ccc(NC(C)=O)cc4)C3)n2)cc(OC)c1OC,,4.574774333558005,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.593842407208746,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+234,ZDG_7_47_2,CN(C)C(=O)c1cccc(-c2cn(C)c3ncnc(N)c23)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+235,PAR_321,Cc1ccc(Nc2nccc(-c3ccc(C(=O)NCC#N)cc3)n2)cc1O,,6.009589815676634,,4.917865976255178,,4.864457187824321,,5.8264947165744125,,4.973637762948251,,6.774955460551948,<,4.522878745280337,,5.255202138876709,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
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+237,ZDG_7_42_1,Cc1ccccc1CC(=O)N1CCc2cc(-c3cn(C)c4ncnc(N)c34)ccc21,,5.61231584009862,,5.156010728689806,,6.243550038680226,<,4.522878745280337,<,4.522878745280337,,5.097975280610396,<,4.522878745280337,<,4.522878745280337,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+238,Derazantinib,COCCNCCc1cccc(Nc2ncc3c(n2)-c2ccccc2[C@H](c2ccccc2F)C3)c1,,4.946677510473712,,4.659559714931235,,5.083215299969158,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+239,PAR_345,O=C(Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)C1(c2ccccc2)CCC1,,5.805406688792475,,5.103777576542542,,4.996365992609234,,6.423519472860726,,5.1412836734445975,,7.202756541176565,<,4.522878745280337,,5.409960957047747,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0
+240,ZDG_6_67,COc1ccc(-c2nn(C)c3cnc(Nc4cc(OC)c(OC)c(OC)c4)nc23)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,6.927259857017201,<,4.522878745280337,,5.156576221429403,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
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+242,PAR_381,CN(CCN1CCCC1)C(=O)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+243,ZDG_6_50_4,COc1cc(Nc2ncc3c(-c4cccc(NS(=O)(=O)CC(F)(F)F)c4)nn(C)c3n2)cc(OC)c1OC,,6.782040430564839,,5.692492267454069,,6.9100886213863655,,6.881171851093462,,5.440548237561658,<,4.522878745280337,<,4.522878745280337,,7.078945066753827,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0
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+247,ZDG_6_48_2,CCS(=O)(=O)Nc1cccc(-c2nn(C)c3nc(Nc4cc(OC)c(OC)c(OC)c4)ncc23)c1,,7.080746134041689,,5.695443092049875,,6.770898194393608,,6.995989692893522,,5.648842078920682,<,4.522878745280337,<,4.522878745280337,,7.016264366350998,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0
+248,PAR_370,O=C(CN1CCOCC1)Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,4.826213105920755,,4.816469259671949,<,4.522878745280337,,5.711795001781335,<,4.522878745280337,,6.008672458852921,<,4.522878745280337,,4.90328024809868,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+249,PAR_241,O=C1CCC(C(=O)Nc2ccc(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cc2)C1,,5.088605527511813,,4.628056532634228,<,4.522878745280337,,5.73702692588722,<,4.522878745280337,,6.133812442366555,<,4.522878745280337,,5.299515894450547,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+250,ZDG_7_26_1,CCNC(=O)c1cccc(COc2cc(-n3cnc4cc(OC)c(OC)cc43)sc2C(N)=O)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+251,Tpl2_Kinase_Inhibitor_1,N#Cc1cnc2cnc(NCc3cccnc3)cc2c1Nc1ccc(F)c(Cl)c1,<,4.522878745280337,,5.025389240195332,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+252,BA_03_61_10,Cc1cc(=O)n(C2CCOCC2)c2nc(Nc3ccc(C(=O)NC(C)C)cc3)ncc12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+253,ZDG_7_46_1,Cn1cc(-c2cn(C)c3ncnc(N)c23)cn1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+254,HO_N_135_1_A,NC(=O)c1cccnc1Nc1nc(-c2cccc(C(F)(F)F)c2)cc2nccn12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+255,PAR_162,Cc1nocc1C(=O)N[C@H]1CCN(C(=O)Nc2nc3c(s2)-c2nc(-c4ccccc4Br)ncc2CC3)C1,,5.555378610964486,,5.133606152079814,,5.025422728526052,,5.887663815348757,,4.955427327315712,,6.635858913662164,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+256,3842,COc1cc(Nc2nccc(-c3ccc4[nH]ccc4c3)n2)cc(C(F)(F)F)c1,,,,,,,,,,,,5.917933065714887,,,,,,,,,,0.0,,
+257,BA_03_50_11,COc1ccc2ncnc(NCCc3ccncc3)c2c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+258,AMG_47a,Cc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1-c1ccc2nc(NCCN3CCOCC3)ncc2c1,<,4.522878745280337,,4.772798973483567,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+259,Golvatinib,CN1CCN(C2CCN(C(=O)Nc3cc(Oc4ccc(NC(=O)C5(C(=O)Nc6ccc(F)cc6)CC5)c(F)c4)ccn3)CC2)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+260,PAR_372,O=C(Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)c1cccc2c1OCO2,,5.2179944972202765,,5.037654317887743,,4.910870806351179,,5.22120758405103,,5.188357732924196,<,4.522878745280337,<,4.522878745280337,,5.031553392186277,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+261,HO_N_132_3_A,COc1ccc(-c2cc3nccn3c(Nc3ncccc3C(N)=O)n2)cc1,,5.046209872089897,,4.917674305168511,,4.779069386451229,<,4.522878745280337,,4.888429126486219,,5.33088176017039,<,4.522878745280337,,5.067141519727515,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+262,PAR_113,Cc1cccc(CNC(=O)c2ccc(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cc2)c1,,5.217302986321796,,5.110465948069461,,5.028279794192806,,5.292558901588945,,5.149197872069948,,6.055950944435368,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+263,BSc5367,COC(=O)c1cccc(-c2cnc3[nH]cc(-c4cccnc4)c3c2)c1,,5.082177979315596,,4.9655607865278375,,4.73755422206139,,5.032137334346293,<,4.522878745280337,,5.978695546843435,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+264,2096,Fc1cc(-c2ccnc(Nc3ccc(-n4cnc(N5CCOCC5)n4)cc3)n2)cc(N2CCOCC2)c1,,,,,,,,,,,,5.8696662315049934,,,,,,,,,,0.0,,
+265,BA_03_51_18,Fc1ccc2ncnc(NCCc3cccc(Br)c3)c2c1,<,4.522878745280337,,6.107209089137724,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+266,Casein_Kinase_II_Inhibitor_IV,COc1cc(Nc2ncc3ccn(-c4cccc(CCC#N)c4)c3n2)cc(OC)c1OC,,6.622840508828964,,5.183577810854469,,6.0280015818133,,5.806834079003082,,5.539397807365343,,7.208527440788626,<,4.522878745280337,,6.924195590604335,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0
+267,PAR_316,COc1ccc(Nc2nccc(-c3ccc(C(=O)NCC#N)cc3)n2)cc1Cl,,5.798704140084891,,5.048846842351343,,4.859488180352635,,5.611174510937102,,5.058627743056515,,6.428120192862645,<,4.522878745280337,,5.214629733947848,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+268,BI_882370,CCN1CCC(N(C)c2ccc3c(n2)c(-c2cncnc2)cn3-c2c(F)ccc(NS(=O)(=O)CC)c2F)CC1,,4.69122285715209,,4.890492432107752,,4.769411612209313,<,4.522878745280337,,4.799019010064749,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+269,3732,N#Cc1c(Nc2nc(Nc3ccc(C4CCNCC4)cc3)ncc2Cl)cccc1OCc1ccccc1F,,,,,,7.30102999566398,,,,,,,,,,,,,1.0,,,,,
+270,S116836,Cc1ccc(C(=O)Nc2cc(-n3ccnc3)cc(C(F)(F)F)c2)cc1C#Cc1cnc(NC2CC2)nc1,,4.733622324873981,<,4.522878745280337,,4.890410185201036,,4.930514495386641,,4.970140129981151,<,4.522878745280337,<,4.522878745280337,,4.832507505969726,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+271,PAR_351,Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccn(C)n3)ncc12,,5.444699172100818,<,4.522878745280337,,5.586962035966202,<,4.522878745280337,,4.763486558006579,,6.379269097867836,<,4.522878745280337,,5.704715854657756,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+272,BA_03_50_19,COc1cccc(CCNc2ncnc3ccc(OC)cc23)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+273,PAR_356,Cc1cc(=O)n(C2CCCC2)c2nc(Nc3cccc(CN4CCOCC4)c3)ncc12,,4.70436486658277,<,4.522878745280337,,5.198768119199553,<,4.522878745280337,<,4.522878745280337,,5.756047241264547,<,4.522878745280337,,4.896988634663503,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+274,BA_03_50_07,COc1cccc([C@H](C)Nc2ncnc3ccc(OC)cc23)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+275,HO_N_138_2_A,Cn1cnc2ccc(-c3cc4nccn4c(Nc4ncccc4C(N)=O)n3)cc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+276,HO_N_73,COc1ccc(-c2cc3nccn3c(Nc3ccncc3C(N)=O)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+277,BA_03_65_a,COC(=O)c1sc(-n2cnc3cc(OC)c(OC)cc32)cc1OCc1sccc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+278,ZDG_6_38,COc1cc(Nc2ncc3c(-c4cccc([N+](=O)[O-])c4)nn(C)c3n2)cc(OC)c1OC,,5.352601318996579,<,4.522878745280337,,4.724733398906056,,5.920407115946844,<,4.522878745280337,,7.0345314539649815,<,4.522878745280337,,5.703723860616155,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+279,EF_3_201,O=C(COc1ccccc1)N[C@H]1CCN(c2ccnc(Nc3ccc(F)cc3)n2)C1,<,4.522878745280337,,4.849411098115262,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+280,TL01_023,CC(=O)Nc1ccc(Nc2nccc(-c3ccc(S(=O)(=O)NCCN)cc3)n2)cc1,,4.684514698854729,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.526497375134073,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+281,PAR_333,COc1cc(Nc2nccc(-c3ccc(NC(=O)C4CCCN4)cc3)n2)ccc1N1CCOCC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+282,PAR_327,O=C(Nc1ccc(-c2ccnc(Nc3ccc(Cl)cc3)n2)cc1)C1CCCN1,<,4.522878745280337,,4.731767974323136,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.051982890328434,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+283,ZDG_6_48_3,COc1cc(Nc2ncc3c(-c4cccc(NC(C)=O)c4)nn(C)c3n2)cc(OC)c1OC,,6.277591921572145,,5.494845765107401,,6.151241698278676,,6.15745762658822,,5.309162923367781,,7.884845915322762,<,4.522878745280337,,6.713127839372918,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0
+284,HO_N_71,COc1ccc(-c2cc3nccn3c(Nc3cnccc3C(N)=O)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+285,ZDG_7_24_2,COc1cc2ncn(-c3cc(OCc4cc(C(F)(F)F)cc(C(F)(F)F)c4)c(C(N)=O)s3)c2cc1OC,,4.536065565647475,<,4.522878745280337,,4.565454954825468,<,4.522878745280337,,4.580553530573381,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+286,BA_03_59_01,COc1cc(Nc2ncc3c(C)cc(=O)n(C4CCC4)c3n2)cc(OC)c1OC,,5.8095204295214895,,4.793359288759993,,6.554206434466748,,4.788746293579548,,4.903651125964368,,6.693623348897365,<,4.522878745280337,,6.01253545631465,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0
+287,HO_N_135_8_A,NC(=O)c1cccnc1Nc1nc(-c2cc(C(F)(F)F)cc(C(F)(F)F)c2)cc2nccn12,<,4.522878745280337,,4.693649170809873,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+288,ZDG_7_15,Cn1cc(-c2cccc(C(F)(F)F)c2)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+289,PAR_395,O=C(COc1ccccc1)N[C@H]1CCN(c2ccnc(Nc3cccc(N4CCOCC4)c3)n2)C1,,4.8730652351051305,,4.789935576943914,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+290,ZDG_6_59_3,COc1cc(Nc2ncc3c(n2)c(C#CCN(C)C)nn3C)cc(OC)c1OC,,4.925516752117212,<,4.522878745280337,,5.229719574217726,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.444903582758697,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+291,PAR_309,N#CCNC(=O)c1ccc(-c2ccnc(Nc3cccc(N4CCOCC4)c3)n2)cc1,,5.89300432206647,,4.898927360316853,,5.175955362843072,,5.839884473737776,,4.969805758716476,,6.755038622864529,<,4.522878745280337,,5.623268330211212,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+292,BA_03_61_05,CS(=O)(=O)c1cccc(Nc2ncc3ccc(=O)n(C4CCOCC4)c3n2)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+293,PAR_336,CC(C)Oc1ccc(Nc2nccc(-c3ccc(NC(=O)C4CCCN4)cc3)n2)cc1,<,4.522878745280337,,5.064189055843583,<,4.522878745280337,,5.697169228177547,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+294,ZDG_7_40_B,Cn1cc(-c2ccc3c(c2)CCN3C(=O)Cc2cccc3c2OCO3)c2c(N)ncnc21,,5.285138286337057,,4.944511813241703,,5.359068800417957,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+295,PAR_138,O=C(Nc1cc(O)[nH]n1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+297,BA_03_56_04,CCOc1ccc2c(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cnn2n1,,6.382563467723347,,5.442808122452164,,6.023964920206267,,6.716551966323366,,5.070433254155037,,7.804781549199357,<,4.522878745280337,,6.817368767842395,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0
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+347,Ifidancitinib,COc1cc(Nc2ncc(C)c(Nc3ccc4oc(=O)[nH]c4c3)n2)cc(C)c1F,,5.387247664524138,,4.832264495121284,,4.5228994754347385,,5.030229217768204,,4.577296369141819,,5.298208185567936,<,4.522878745280337,,5.179591189738451,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+348,ZDG_6_66,COc1ccccc1Nc1ncc2c(n1)c(-c1cccc(NS(C)(=O)=O)c1)nn2C,,5.138546774720314,,4.971941526406618,,5.006132125882433,,4.928694690643835,,4.987415237762792,,6.614735179247774,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+349,HO_N_140,COc1ccc(-c2cc3nccn3c(Nc3ncccc3C(=O)O)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+350,HO_N_62,COc1ccc(-c2cc3nccn3c(Nc3ccccn3)n2)cc1OC,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+351,ZDG_7_35_C,Cn1cc(-c2ccc3c(c2)CCN3C(=O)C2C(C)(C)C2(C)C)c2c(N)ncnc21,,4.931814837850233,,5.046661835207546,,5.472401646550287,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+352,GSK461364,C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+353,3823,CCOc1ccc2c(-c3ccnc(Nc4ccc(C(N)=O)cc4)n3)cnn2n1,,,,,,,,,,,,7.075720713938117,,,,,,,,,,1.0,,
+354,PAR_141,O=C(NCC1CCC(F)(F)CC1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+355,ZDG_7_26_3,CCN(C(=O)COc1cc(-n2cnc3cc(OC)c(OC)cc32)sc1C(N)=O)c1cccc(C)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+356,BA_03_50_03,COc1ccc2ncnc(NC3[C@H]4C[C@@H]5C[C@@H](C[C@H]3C5)C4)c2c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+357,HO_N_133_3_A,COc1cccc(-c2cc3nccn3c(Nc3ncccc3C(N)=O)n2)c1OC,,4.636953072082088,,4.612097017819476,,4.705893907584375,,5.101435565833748,<,4.522878745280337,,5.346187827681289,<,4.522878745280337,,5.08700015944291,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+358,PAR_378,O=C(Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)C1CC1,,5.795223621307058,,5.069636181663933,,5.189563149194148,,5.354112429809823,,5.090225183808611,,6.435809867643536,<,4.522878745280337,,5.320880002769998,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+359,PAR_396,CN(C)S(=O)(=O)c1cccc(Nc2nccc(N3CC[C@H](NC(=O)COc4ccccc4)C3)n2)c1,,5.639804514613448,,5.698986663575901,,5.589807383635014,<,4.522878745280337,,5.485285553246834,<,4.522878745280337,<,4.522878745280337,,5.391752647388778,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+360,PAR_248,O=C(Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)C1CCOCC1,,4.7701367862789015,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.539222443114369,<,4.522878745280337,,4.828484144000552,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+361,ZDG_7_34_B,Cn1cc(-c2ccc3c(c2)CCN3C(=O)C2CCC(F)(F)CC2)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,,4.738800287970558,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+362,PAR_353,Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(CN4CCOCC4)cc3)ncc12,,4.889178578148,,4.694100978880336,,5.521752502766849,<,4.522878745280337,<,4.522878745280337,,5.609121210067395,<,4.522878745280337,,5.216615473969439,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+363,PAR_140,O=C(c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1)N1CC[C@@H](O)C1,,4.947851149814153,<,4.522878745280337,<,4.522878745280337,,4.9167148935059615,<,4.522878745280337,,5.738652702744123,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+364,ZDG_7_41_3,Cn1cc(-c2ccc3c(c2)CCN3C(=O)Cc2ccc3c(c2)OCCO3)c2c(N)ncnc21,,5.009846998035169,,5.068850956510059,,5.460293990716453,<,4.522878745280337,,4.979278393101506,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+365,ON1231320,Cn1c(=O)c(S(=O)(=O)c2ccc(F)cc2F)cc2cnc(Nc3ccc4[nH]ccc4c3)nc21,,5.414618604428587,,5.277990016375206,,5.399388668716516,,5.37920890878158,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.257878075412741,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+366,BA_03_61_03,Cc1cc(=O)n(C2CCOCC2)c2nc(Nc3ccc(N4CCOCC4)cc3)ncc12,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+367,PAR_81,O=C(Nc1nc2c(s1)-c1nc(-c3ccccc3Br)ncc1CC2)NC1CCCNC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+368,ZDG_7_51_7,COc1cc(Nc2ncc3c(C)cc(=O)n(C4CCCC4)c3n2)cc(OC)c1OC,,5.844613985129847,<,4.522878745280337,,6.002273976255355,,4.53953279402968,,5.039361792838563,,6.680243093632786,<,4.522878745280337,,6.224836377308716,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0
+369,ZDG_6_72,COc1cc(Nc2ncc3c(n2)c(C2=CCOCC2)nn3C)cc(OC)c1OC,,5.256698859829601,<,4.522878745280337,,5.408457936833549,,5.050230571632087,<,4.522878745280337,,7.322740251210563,<,4.522878745280337,,5.217736958089621,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+370,ZDG_7_53_1,CN1CCN(c2cccc(Nc3nccc(-c4ccc(NC(=O)[C@@H]5CCCN5)cc4)n3)c2)CC1,,5.338173223355024,<,4.522878745280337,<,4.522878745280337,,6.231164950149952,<,4.522878745280337,,6.334276757369631,<,4.522878745280337,,5.32844855319665,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0
+371,Nimucitinib,NC(=O)c1cnc(Nc2cccc(NC(=O)[C@H]3CCCNC3)c2)cc1NCc1cc(F)cc(F)c1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.651869225303184,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+372,HO_N_83,CN1CCN(c2ccc(Nc3ncc4c(n3)c(-c3cccc(NS(C)(=O)=O)c3)nn4C)cc2)CC1,,5.561448562355972,<,4.522878745280337,,6.155927167866227,,6.479296566675651,<,4.522878745280337,,7.354332708971508,<,4.522878745280337,,6.592940999627449,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0
+373,PAR_110,O=C(NC1CCCNC1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+374,ZDG_6_41,COc1cc(Nc2ncc3c(-c4cccc(N)c4)nn(C)c3n2)cc(OC)c1OC,,6.072137063704354,,5.331158920986598,,6.41637171103705,,6.192495604580524,,5.327101828744807,,7.769368396562845,<,4.522878745280337,,6.210385953351892,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0
+375,ZDG_7_47_3,Cn1cc(-c2ccc3c(c2)OCCO3)c2c(N)ncnc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+376,3856,Cc1cnc(Nc2ccc(NC(=O)c3cccc(NC(=O)/C=C/CN(C)C)c3)cc2)nc1-c1cccnc1,,,,,,,,,,,,5.549750891680638,,,,,,,,,,0.0,,
+377,BA_03_53_04,COc1ccc2c(-c3ccnc(Nc4ccc(N5CCOCC5)cc4)n3)cnn2n1,,5.824924592355906,,5.209294342423722,,5.663139266208908,,6.3290190292376,,4.837143150361911,<,4.522878745280337,<,4.522878745280337,,6.6043667019657,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0
+378,ZDG_7_9,Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(C(=O)NCC(F)F)cc3)ncc12,,4.956505457771383,,4.703039486786711,,4.990639364692515,<,4.522878745280337,,4.74687777691037,,6.067682583262696,<,4.522878745280337,,5.269688804153579,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+379,ZDG_7_37_C,Cn1cc(-c2ccc3c(c2)CCN3C(=O)CC2Cc3ccccc3C2)c2c(N)ncnc21,,5.105383633393123,,5.257293748542373,,5.378444490078154,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+380,ZDG_7_50_5,CN1CCN(C(=O)c2ccc(Nc3nccc(-c4ccc(NC(=O)[C@@H]5CCCN5)cc4)n3)cc2)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.154876214572618,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+381,ZDG_7_50_3,CN1CCN(c2ccc(Nc3nccc(-c4ccc(NC(=O)[C@@H]5CCCN5)cc4)n3)cc2)CC1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,5.493317766712035,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,4.58678479491856,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+382,PAR_108,O=C(NC1CCCCCC1)c1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,4.648976885597432,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+383,TIE_2_VEGFR_2_kinase_IN_2,Nc1ncnc2occ(-c3ccc(NC(=O)Nc4cc(C(F)(F)F)ccc4F)cc3)c12,,4.923856956015497,,5.279845965163705,,5.358873766581655,<,4.522878745280337,<,4.522878745280337,,5.275306784282457,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+384,BA_03_65_b,COC(=O)c1sc(-n2cnc3cc(OC)c(OC)cc32)cc1OCc1ccc(-c2ccncc2)s1,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+385,ZDG_7_36_C,CC#CC(=O)N1CCc2cc(-c3cn(C)c4ncnc(N)c34)ccc21,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+386,3739,N#Cc1c(Nc2nc(Nc3ccc(N4CCNCC4)cc3)ncc2Cl)cccc1OCc1cccc(F)c1,,,,,,6.823908740944318,,,,,,,,,,,,,1.0,,,,,
+387,ZDG_6_48_1,COc1cc(Nc2ncc3c(-c4cccc(NS(=O)(=O)C5CC5)c4)nn(C)c3n2)cc(OC)c1OC,,7.134526430898649,<,4.522878745280337,,6.827566657475918,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,,6.980961650786502,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0
+388,3743,N#Cc1c(Nc2nc(Nc3cnn(C4CCNCC4)c3)ncc2Cl)cccc1OCc1ccccc1F,,,,,,6.823908740944318,,,,,,,,,,,,,1.0,,,,,
+389,ZDG_7_23_2,COc1cc2ncn(-c3cc(OCc4ccc(F)cc4)c(C(N)=O)s3)c2cc1OC,<,4.522878745280337,<,4.522878745280337,,4.595719696676417,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+390,PAR_408,COc1cc(Nc2nccc(N3CC[C@H](NC(=O)c4ccc(C#N)cc4)C3)n2)cc(OC)c1OC,,5.598642092872625,,4.692209917210851,,4.864752009196747,<,4.522878745280337,,4.769202671483697,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+391,3821,CCOc1ccc2c(-c3ccnc(Nc4ccc(N5CCN(C)CC5)cc4)n3)cnn2n1,,,,,,,,,,,,7.236572006437062,,,,,,,,,,1.0,,
+392,BA_03_66_c,COc1cc2ncn(-c3cc(OCc4sccc4F)c(C(N)=O)s3)c2cc1OC,<,4.522878745280337,,4.534933246450109,,4.987977287080152,<,4.522878745280337,,4.6026568572406905,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+393,PAR_149,O=C(Nc1nc2c(s1)-c1nc(-c3ccccc3Br)ncc1CC2)N1CCC(N2CCCCC2)CC1,,4.803895504413972,<,4.522878745280337,,4.70292748711545,,4.937318844782486,<,4.522878745280337,,5.305326640282966,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+394,PAR_247,CCOc1ccsc1C(=O)Nc1ccc(-c2ccnc(Nc3ccc(N4CCOCC4)cc3)n2)cc1,,5.213153750968695,,5.065611488315886,,4.91383756830168,,5.220674896840294,,5.14240597996013,,5.22581934838519,<,4.522878745280337,,5.141058054333915,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+395,CHZ868,CC(=O)Nc1cc(Oc2ccc3c(nc(Nc4ccc(F)cc4F)n3C)c2C)ccn1,<,4.522878745280337,,4.73755906290163,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,<,4.522878745280337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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diff --git a/atomsci/ddm/test/integrative/sampling_test/nanobret_multitask_classification_data_train_valid_test_multitaskscaffold_e34ba827-a532-4313-9e63-8a9b0ed18ba9.csv b/atomsci/ddm/test/integrative/sampling_test/nanobret_multitask_classification_data_train_valid_test_multitaskscaffold_e34ba827-a532-4313-9e63-8a9b0ed18ba9.csv
new file mode 100755
index 00000000..9699217d
--- /dev/null
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diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_NN_SMOTE.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_NN_SMOTE.json
new file mode 100644
index 00000000..41febfb6
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_NN_SMOTE.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random",
+"sampling_method": "SMOTE", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_NN_undersampling.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_NN_undersampling.json
new file mode 100644
index 00000000..86f7bcf2
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_NN_undersampling.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"sampling_method": "undersampling",
+"smiles_col": "base_rdkit_smiles",
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_RF_SMOTE.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_RF_SMOTE.json
new file mode 100644
index 00000000..d2b95fb8
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_RF_SMOTE.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"sampling_method": "SMOTE",
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_RF_undersampling.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_RF_undersampling.json
new file mode 100644
index 00000000..fda25eff
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_RF_undersampling.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"sampling_method": "undersampling",
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"smiles_col": "base_rdkit_smiles",
+"id_col": "compound_id", 
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_xgboost_SMOTE.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_xgboost_SMOTE.json
new file mode 100644
index 00000000..dafca23c
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_xgboost_SMOTE.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"sampling_method": "SMOTE",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_xgboost_undersampling.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_xgboost_undersampling.json
new file mode 100644
index 00000000..14b43f1c
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/kfold_cv_xgboost_undersampling.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"sampling_method": "undersampling",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_NN_SMOTE.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_NN_SMOTE.json
new file mode 100644
index 00000000..7fdbb4d3
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_NN_SMOTE.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"splitter": "scaffold", 
+"sampling_method": "SMOTE",
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_NN_undersampling.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_NN_undersampling.json
new file mode 100644
index 00000000..bd771283
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_NN_undersampling.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"sampling_method": "undersampling",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_RF_SMOTE.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_RF_SMOTE.json
new file mode 100644
index 00000000..88a761f7
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_RF_SMOTE.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"sampling_method": "SMOTE",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_RF_undersampling.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_RF_undersampling.json
new file mode 100644
index 00000000..e88717a1
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_RF_undersampling.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"sampling_method": "undersampling",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_xgboost_SMOTE.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_xgboost_SMOTE.json
new file mode 100644
index 00000000..8845062f
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_xgboost_SMOTE.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"sampling_method": "SMOTE",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_xgboost_undersampling.json b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_xgboost_undersampling.json
new file mode 100644
index 00000000..840333e1
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/sampling_json/train_valid_test_xgboost_undersampling.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"sampling_method": "undersampling",
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"response_cols":"active", 
+"smiles_col": "base_rdkit_smiles",
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/test_sampling.py b/atomsci/ddm/test/integrative/sampling_test/test_sampling.py
new file mode 100644
index 00000000..d859a4ac
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/test_sampling.py
@@ -0,0 +1,317 @@
+#!/usr/bin/env python 
+"""Testing the sampling methods. Want to ensure that the model pipeline works and that the sampling methods are incorporated.
+Based off of the test_kfold_split.py method. """
+import sklearn.metrics as skmetrics 
+import copy
+import os
+import json 
+
+from atomsci.ddm.pipeline import model_pipeline as mp
+from atomsci.ddm.pipeline import parameter_parser as parse
+import atomsci.ddm.pipeline.predict_from_model as pfm
+
+import sys
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+from integrative_utilities import extract_seed, get_test_set, find_best_test_metric
+
+#-------------------------------------------------------------------
+
+def saved_model_identity(pparams):
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    retrain_pparams = copy.copy(pparams)
+
+    model_pipe = mp.ModelPipeline(pparams)
+
+    if not pparams.previously_split:
+        split_uuid = model_pipe.split_dataset()
+        pparams.split_uuid = split_uuid
+        pparams.previously_split = True
+        pparams.split_only=False
+
+    model_pipe.train_model()
+    
+    split_csv = os.path.join(script_path, '../../test_datasets/', model_pipe.data._get_split_key())
+    test_df = get_test_set(pparams.dataset_key, split_csv, pparams.id_col)
+
+    # load model metrics from file 
+    with open(os.path.join(pparams.output_dir, 'model_metrics.json'), 'r') as f:
+        model_metrics = json.load(f)
+    
+    metrics = find_best_test_metric(model_metrics)
+    original_accuracy = metrics['prediction_results']['accuracy_score']
+    original_precision = metrics['prediction_results']['precision']
+    original_recall = metrics['prediction_results']['recall_score']
+    original_prc_auc = metrics['prediction_results']['prc_auc_score']
+    
+    id_col = metrics['input_dataset']['id_col']
+    response_col=metrics['input_dataset']['response_cols'][0]
+    smiles_col = metrics['input_dataset']['smiles_col']
+    test_length = metrics['prediction_results']['num_compounds']
+
+    # predict from model
+    model_tar = model_pipe.params.model_tarball_path
+    pred_df = pfm.predict_from_model_file(model_tar, test_df, id_col=id_col,
+                smiles_col=smiles_col, response_col=response_col)
+    # generate another prediction from the same model file
+    pred_df2 = pfm.predict_from_model_file(model_tar, test_df, id_col=id_col, smiles_col=smiles_col, response_col=response_col)
+
+    X = pred_df[response_col+'_actual'].values
+    y = pred_df[response_col+'_pred'].values
+
+    accuracy = skmetrics.accuracy_score(X, y)
+    precision = skmetrics.precision_score(X, y, average='weighted')
+    recall = skmetrics.recall_score(X, y, average='weighted')
+    prc_auc = skmetrics.average_precision_score(X, y)
+
+    # return the metrics from the second prediction
+    X2 = pred_df2[response_col+'_actual'].values
+    y2 = pred_df2[response_col+'_pred'].values
+
+    x2_accuracy = skmetrics.accuracy_score(X2, y2)
+    x2_precision = skmetrics.precision_score(X2, y2, average='weighted')
+    x2_recall = skmetrics.recall_score(X2, y2, average='weighted')
+    x2_prc_auc = skmetrics.average_precision_score(X2, y2)
+
+    #saved_accuracy = metrics['prediction_results']['accuracy_score']
+    #saved_precision = metrics['prediction_results']['precision']
+    #saved_recall = metrics['prediction_results']['recall_score']
+    #saved_prc_auc = metrics['prediction_results']['prc_auc_score']
+
+    # show results and compare the two predictions 
+    print(metrics['subset'])
+    print(pred_df.columns)
+    print("Prediction results")
+    print("Accuracy difference:", abs(accuracy - x2_accuracy))
+    print("Precision difference:", abs(precision - x2_precision))
+    print("Recall difference:", abs(recall-x2_recall))
+    print("PRC AUC difference:", abs(prc_auc-x2_prc_auc))
+
+    assert abs(accuracy - x2_accuracy) < 1e-9 \
+        and abs(precision - x2_precision) < 1e-9 \
+        and abs(recall - x2_recall) < 1e-9 \
+        and abs(prc_auc - x2_prc_auc) < 1e-9 \
+        and (test_length == len(test_df))
+
+    # create another test to ensure that the sampling methods are reproducible with the seed 
+    metadata_path = os.path.join(pparams.output_dir, 'model_metadata.json')
+    seed = extract_seed(metadata_path)
+
+    # create a duplicate parameters and add the seed
+    retrain_pparams.seed = seed
+    retrain_pparams.model_uuid = None 
+
+    # retrain the model
+    retrain_pipe = mp.ModelPipeline(retrain_pparams)
+    retrain_pipe.train_model()
+
+    # extract the metrics from the retrained model
+    with open(os.path.join(retrain_pparams.output_dir, 'model_metrics.json'), 'r') as f:
+        retrained_model_metrics = json.load(f)
+
+    retrained_metrics = find_best_test_metric(retrained_model_metrics)    
+    retrained_accuracy = retrained_metrics['prediction_results']['accuracy_score']
+    retrained_precision = retrained_metrics['prediction_results']['precision']
+    retrained_recall = retrained_metrics['prediction_results']['recall_score']       
+    retrained_prc_auc = retrained_metrics['prediction_results']['prc_auc_score']
+    
+    print("Model reproducibility results")
+    print("Accuracy difference:", abs(original_accuracy-retrained_accuracy))
+    print("Precision difference:", abs(original_precision-retrained_precision))
+    print("Recall difference:", abs(original_recall-retrained_recall))
+    print("PRC AUC difference:", abs(original_prc_auc-retrained_prc_auc))
+    
+    assert abs(original_accuracy - retrained_accuracy) < 1e-9 \
+        and abs(original_precision - retrained_precision) < 1e-9 \
+        and abs(original_recall - retrained_recall) < 1e-9 \
+        and abs(original_prc_auc - retrained_prc_auc) < 1e-9
+#-------------------------------------------------------------------
+
+#-------- random forest
+def test_train_valid_test_RF_SMOTE(): 
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/train_valid_test_RF_SMOTE.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_k_fold_cv_RF_SMOTE():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/kfold_cv_RF_SMOTE.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_k_fold_cv_RF_undersampling():
+
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/kfold_cv_RF_undersampling.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_train_valid_test_RF_undersampling(): 
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/train_valid_test_RF_undersampling.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+#-------- neural network
+
+def test_train_valid_test_NN_SMOTE():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/train_valid_test_NN_SMOTE.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_train_valid_test_NN_undersampling():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/train_valid_test_NN_undersampling.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_k_fold_cv_NN_SMOTE():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/kfold_cv_NN_SMOTE.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_k_fold_cv_NN_undersampling():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/kfold_cv_NN_undersampling.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+#-------- xgboost
+
+def test_train_valid_test_xgboost_SMOTE():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/train_valid_test_xgboost_SMOTE.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_train_valid_test_xgboost_undersampling():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/train_valid_test_xgboost_undersampling.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_k_fold_cv_xgboost_SMOTE():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/kfold_cv_xgboost_SMOTE.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+
+def test_k_fold_cv_xgboost_undersampling():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file = os.path.join(script_path, 'sampling_json/kfold_cv_xgboost_undersampling.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key= os.path.join(script_path,
+                                      '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+    pparams.split_uuid= 'test-split'
+
+    saved_model_identity(pparams)
+#-------------------------------------------------------------------
+
+if __name__=='__main__':
+    #print('train_valid_test_RF_SMOTE_test')
+    #test_train_valid_test_RF_SMOTE()
+    
+    #print('train_valid_test_NN_SMOTE_test')
+    #test_train_valid_test_NN_SMOTE()
+    
+    #print("train_valid_test_RF_undersampling_test")
+    #test_train_valid_test_RF_undersampling()
+    
+    #print("train_valid_test_NN_undersampling_test")
+    #test_train_valid_test_NN_undersampling()
+    
+    #print("kfold_cv_NN_SMOTE_test")
+    #test_k_fold_cv_NN_SMOTE()
+
+    #print("kfold_cv_NN_undersampling_test")
+    #test_k_fold_cv_NN_undersampling()
+
+    print("kfold_cv_RF_SMOTE_test")
+    test_k_fold_cv_RF_SMOTE()
+
+    #print("kfold_cv_RF_undersampling_test")
+    #test_k_fold_cv_RF_undersampling()
+    
+    #print("train_valid_test_xgboost_SMOTE_test")
+    #test_train_valid_test_xgboost_SMOTE()
+
+    #print("train_valid_test_xgboost_undersampling_test")
+    #test_train_valid_test_xgboost_undersampling()
+
+    #print("k_fold_cv_xgboost_SMOTE_test")
+    #test_k_fold_cv_xgboost_SMOTE()
+
+    #print("k_fold_cv_xgboost_undersampling_test")
+    #test_k_fold_cv_xgboost_undersampling()
+
+    print("Passed!")
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/sampling_test/test_sampling_mtss_model.py b/atomsci/ddm/test/integrative/sampling_test/test_sampling_mtss_model.py
new file mode 100755
index 00000000..d60c4551
--- /dev/null
+++ b/atomsci/ddm/test/integrative/sampling_test/test_sampling_mtss_model.py
@@ -0,0 +1,77 @@
+# smote/undersampling multitask test 
+
+import os
+import atomsci.ddm.pipeline.model_pipeline as mp
+import atomsci.ddm.pipeline.parameter_parser as parse
+import pytest
+
+
+def test_mtss_model():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    dataset_file = os.path.join(script_path, "nanobret_multitask_classification_data.csv")
+    split_uuid="e34ba827-a532-4313-9e63-8a9b0ed18ba9"
+    odir = os.path.join(script_path, "output")
+
+    id_col="compound_id"
+    smiles_col="base_rdkit_smiles"
+    response_cols="NEK1_active,NEK2_active,NEK3_active,NEK5_active,NEK9_active"
+
+    params = {
+        # logistics input
+        "dataset_key": dataset_file, 
+        "smiles_col": smiles_col,
+        "prediction_type": "classification",
+        "split_uuid": split_uuid, 
+        "splitter": "multitaskscaffold",
+        "response_cols": response_cols, 
+        "previously_split": "True",
+
+        # dataset
+        "id_col": id_col,
+        "result_dir": odir, 
+
+        # featurization and model 
+        "featurizer": "computed_descriptors",
+        "descriptor_type": "rdkit_raw",
+        "model_type": "NN",
+        # grid search 
+        "max_epochs": "300",
+        "early_stopping_patience": "100",
+        "sampling_method":"SMOTE",
+        "layer_sizes": "128,128,128",
+        "dropouts": "0.1,0.1,0.10",
+        "learning_rates": "0.0007",
+
+        # extras, can be deleted as needed
+        "system": "LC", 
+        "verbose": "True",
+    }
+    ampl_param = parse.wrapper(params)
+    pl = mp.ModelPipeline(ampl_param)
+    with pytest.raises(ValueError) as e:
+        # this should say
+        # Imbalanced-learn currently supports binary, multiclass and binarized encoded multiclasss targets. Multilabel and multioutput targets are not supported.
+        pl.train_model()
+    print("done")
+
+def test_imblearn_mtss_compatibility():
+    # this just shows that all SMOTE methods do not work with multitask problems.
+    import sklearn.datasets as skdatasets
+    import numpy as np
+    from imblearn.over_sampling import SMOTE, ADASYN, BorderlineSMOTE, KMeansSMOTE, SVMSMOTE
+
+    X, y = skdatasets.make_classification()
+    print(X.shape, y.shape)
+    multi_y = np.vstack([y, y, y]).transpose()
+    print(multi_y.shape)
+
+    for sampler in [SMOTE, ADASYN, BorderlineSMOTE, KMeansSMOTE, SVMSMOTE]:
+        sm = sampler()
+        try:
+            _x, _y = sm.fit_resample(X, multi_y)
+        except Exception as e:
+            print(sm)
+            print(e)
+
+if __name__ == "__main__":
+    test_mtss_model()
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/attentivefp_regression_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/attentivefp_regression_train_valid_test.json
new file mode 100644
index 00000000..c87a245b
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/attentivefp_regression_train_valid_test.json
@@ -0,0 +1,19 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "AttentiveFPModel",
+"AttentiveFPModel_num_layers":"3",
+"AttentiveFPModel_learning_rate": "0.0007",
+"AttentiveFPModel_n_tasks": "1",
+"featurizer":"MolGraphConvFeaturizer",
+"MolGraphConvFeaturizer_use_edges":"True",
+"prediction_type": "regression",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"15"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/graphconv_classification_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/graphconv_classification_train_valid_test.json
new file mode 100644
index 00000000..7f0380e2
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/graphconv_classification_train_valid_test.json
@@ -0,0 +1,16 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "graphconv", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"uncertainty":"False",
+"max_epochs":"15"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/graphconv_regression_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/graphconv_regression_train_valid_test.json
new file mode 100644
index 00000000..3fd351fd
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/graphconv_regression_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "graphconv", 
+"prediction_type": "regression",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"15"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/nn_classification_kfold_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/nn_classification_kfold_test.json
new file mode 100644
index 00000000..7ce26e8b
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/nn_classification_kfold_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/nn_classification_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/nn_classification_train_valid_test.json
new file mode 100644
index 00000000..c3eda35a
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/nn_classification_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/nn_regression_kfold_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/nn_regression_kfold_test.json
new file mode 100644
index 00000000..dede5f7b
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/nn_regression_kfold_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "regression",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/nn_regression_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/nn_regression_train_valid_test.json
new file mode 100644
index 00000000..ba86fcce
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/nn_regression_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "NN", 
+"featurizer": "ecfp", 
+"prediction_type": "regression",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/pytorchmpnn_regression_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/pytorchmpnn_regression_train_valid_test.json
new file mode 100644
index 00000000..20b2e0b2
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/pytorchmpnn_regression_train_valid_test.json
@@ -0,0 +1,19 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "PytorchMPNNModel",
+"PytrochMPNNModel_mode": "regression",
+"PytorchMPNNModel_learning_rate": "0.001",
+"PytorchMPNNModel_n_tasks": "1",
+"featurizer":"MolGraphConvFeaturizer",
+"MolGraphConvFeaturizer_use_edges":"True",
+"prediction_type": "regression",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"15"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/rf_classification_kfold_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/rf_classification_kfold_test.json
new file mode 100644
index 00000000..dd8f6b64
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/rf_classification_kfold_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/rf_classification_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/rf_classification_train_valid_test.json
new file mode 100644
index 00000000..4777506f
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/rf_classification_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/rf_regression_kfold_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/rf_regression_kfold_test.json
new file mode 100644
index 00000000..30bcc444
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/rf_regression_kfold_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "regression",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/rf_regression_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/rf_regression_train_valid_test.json
new file mode 100644
index 00000000..05415938
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/rf_regression_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "RF", 
+"featurizer": "ecfp", 
+"prediction_type": "regression",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_classification_kfold_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_classification_kfold_test.json
new file mode 100644
index 00000000..7c62dcab
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_classification_kfold_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_classification_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_classification_train_valid_test.json
new file mode 100644
index 00000000..a8072c5b
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_classification_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "classification",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_regression_kfold_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_regression_kfold_test.json
new file mode 100644
index 00000000..7bd80469
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_regression_kfold_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "regression",
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_regression_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_regression_train_valid_test.json
new file mode 100644
index 00000000..a96c72a3
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/model_json/xgboost_regression_train_valid_test.json
@@ -0,0 +1,15 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"model_type": "xgboost", 
+"featurizer": "ecfp", 
+"prediction_type": "regression",
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"pIC50", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/split_json/test_fingerprint_train_valid_test.json b/atomsci/ddm/test/integrative/seed_test/split_json/test_fingerprint_train_valid_test.json
new file mode 100644
index 00000000..bafe7399
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/split_json/test_fingerprint_train_valid_test.json
@@ -0,0 +1,13 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"featurizer": "ecfp", 
+"split_strategy": "train_valid_test", 
+"splitter": "fingerprint", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_fingerprint_split.json b/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_fingerprint_split.json
new file mode 100644
index 00000000..e03a34a0
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_fingerprint_split.json
@@ -0,0 +1,13 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"featurizer": "ecfp", 
+"split_strategy": "k_fold_cv", 
+"splitter": "fingerprint", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_random_split.json b/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_random_split.json
new file mode 100644
index 00000000..352e86c5
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_random_split.json
@@ -0,0 +1,13 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"featurizer": "ecfp", 
+"split_strategy": "k_fold_cv", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_scaffold_split.json b/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_scaffold_split.json
new file mode 100644
index 00000000..00976ccd
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/split_json/test_kfold_scaffold_split.json
@@ -0,0 +1,13 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"featurizer": "ecfp", 
+"split_strategy": "k_fold_cv", 
+"splitter": "scaffold", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/split_json/test_random_train_valid_test_split.json b/atomsci/ddm/test/integrative/seed_test/split_json/test_random_train_valid_test_split.json
new file mode 100644
index 00000000..188a8aa0
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/split_json/test_random_train_valid_test_split.json
@@ -0,0 +1,13 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"featurizer": "ecfp", 
+"split_strategy": "train_valid_test", 
+"splitter": "random", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/split_json/test_scaffold_train_valid_test_split.json b/atomsci/ddm/test/integrative/seed_test/split_json/test_scaffold_train_valid_test_split.json
new file mode 100644
index 00000000..f913cc9d
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/split_json/test_scaffold_train_valid_test_split.json
@@ -0,0 +1,13 @@
+{"verbose": "True",
+"datastore": "False", 
+"save_results": "False", 
+"featurizer": "ecfp", 
+"split_strategy": "train_valid_test", 
+"splitter": "scaffold", 
+"split_test_frac": "0.15", 
+"split_valid_frac": "0.15", 
+"transformers": "True", 
+"id_col": "compound_id", 
+"smiles_col": "base_rdkit_smiles",
+"response_cols":"active", 
+"max_epochs":"100"}
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/test_seed_models.py b/atomsci/ddm/test/integrative/seed_test/test_seed_models.py
new file mode 100644
index 00000000..a53d7397
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/test_seed_models.py
@@ -0,0 +1,319 @@
+#!/usr/bin/env python 
+"""Testing the reproducibility of seeding a random seed in AMPL to reproduce models."""
+import pandas as pd 
+import copy
+import os
+import json
+
+from atomsci.ddm.pipeline import model_pipeline as mp
+from atomsci.ddm.pipeline import parameter_parser as parse 
+
+import sys
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+from integrative_utilities import extract_seed, find_best_test_metric
+
+#-------------------------------------------------------------------
+"""
+This script does the following:
+1. Generates a model 
+2. Extracts the seed from the model's metadata 
+3. Runs the model training again with seed
+4. Compares the prediction scores to ensure they're identical
+
+Creates and tests the following models:
+- RF, NN
+- regression, classification
+- train_valid_test split, k-fold cv split
+"""
+#-------------------------------------------------------------------
+def saved_model_identity(pparams):
+
+    retrain_pparams = copy.copy(pparams)
+
+    model_pipe = mp.ModelPipeline(pparams)
+
+    if not pparams.previously_split:
+        split_uuid = model_pipe.split_dataset()
+        pparams.split_uuid = split_uuid
+        pparams.previously_split = True 
+        pparams.split_only = False
+
+    model_pipe.train_model()
+
+    # load model metrics from file 
+    with open(os.path.join(pparams.output_dir, 'model_metrics.json'), 'r') as f:
+        model_metrics = json.load(f)
+
+    original_metrics = find_best_test_metric(model_metrics)
+    if pparams.prediction_type == 'regression':
+        original_mae = original_metrics['prediction_results']['mae_score']
+        original_r2 = original_metrics['prediction_results']['r2_score']
+        original_rms_score = original_metrics['prediction_results']['rms_score']
+    elif pparams.prediction_type == 'classification':
+        original_accuracy = original_metrics['prediction_results']['accuracy_score']
+        original_precision = original_metrics['prediction_results']['precision']
+        original_recall = original_metrics['prediction_results']['recall_score']
+        original_prc_auc = original_metrics['prediction_results']['prc_auc_score']
+
+
+    # extract the seed 
+    metadata_path = os.path.join(pparams.output_dir, 'model_metadata.json')
+    seed = extract_seed(metadata_path)
+
+    # add the seed to the params
+    retrain_pparams.seed = seed
+    retrain_pparams.model_uuid = None
+
+    # retrain the model
+    retrain_pipe = mp.ModelPipeline(retrain_pparams)
+    retrain_pipe.train_model() 
+    #retrain_pipe = train(pparams)
+
+    # extract the metrics from the retrained model 
+    with open(os.path.join(retrain_pparams.output_dir, 'model_metrics.json'), 'r') as f:
+        retrained_model_metrics = json.load(f)
+    
+    retrained_metrics = find_best_test_metric(retrained_model_metrics)
+    if pparams.prediction_type == 'regression':
+        retrained_mae = retrained_metrics['prediction_results']['mae_score']
+        retrained_r2 = retrained_metrics['prediction_results']['r2_score']
+        retrained_rms_score = retrained_metrics['prediction_results']['rms_score']
+    elif pparams.prediction_type == 'classification':
+        retrained_accuracy = retrained_metrics['prediction_results']['accuracy_score']
+        retrained_precision = retrained_metrics['prediction_results']['precision']
+        retrained_recall = retrained_metrics['prediction_results']['recall_score']
+        retrained_prc_auc = retrained_metrics['prediction_results']['prc_auc_score']
+
+    if pparams.prediction_type == 'regression':
+        print("MAE difference:", abs(original_mae-retrained_mae))
+        print("R2 difference:", abs(original_r2 - retrained_r2))
+        print("RMS Score difference:", abs(original_rms_score - retrained_rms_score))
+
+        assert abs(original_mae-retrained_mae) < 1e-9 \
+            and abs(original_r2 - retrained_r2) < 1e-9 \
+            and abs(original_rms_score - retrained_rms_score) < 1e-9
+        
+    elif pparams.prediction_type == 'classification':
+        print("Accuracy difference:", abs(original_accuracy - retrained_accuracy))
+        print("Precision difference:", abs(original_precision - retrained_precision))
+        print("Recall difference:", abs(original_recall - retrained_recall))
+        print("PRC AUC difference:", abs(original_prc_auc- retrained_prc_auc))
+        
+        assert abs(original_accuracy - retrained_accuracy) < 1e-9 \
+            and abs(original_precision - retrained_precision) < 1e-9 \
+            and abs(original_recall - retrained_recall) < 1e-9 \
+            and abs(original_prc_auc - retrained_prc_auc) < 1e-9
+
+#-------------------------------------------------------------------
+# Random Forest
+def test_RF_regression_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/rf_regression_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_RF_classification_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/rf_classification_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_RF_regression_kfold_cv_reproducibility(): 
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/rf_regression_kfold_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_RF_classification_kfold_cv_reproducibility(): 
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/rf_classification_kfold_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+# Neural Network
+def test_NN_regression_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/nn_regression_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_NN_regression_kfold_cv_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/nn_regression_kfold_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_NN_classification_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/nn_classification_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_NN_classification_kfold_cv_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/nn_classification_kfold_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+# XGBoost 
+def test_xgboost_regression_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/xgboost_regression_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_xgboost_classification_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/xgboost_classification_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_xgboost_regression_kfold_cv_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/xgboost_regression_kfold_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_xgboost_classification_kfold_cv_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/xgboost_classification_kfold_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+# graphconv
+def test_graphconv_classification_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/graphconv_classification_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_graphconv_regression_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/graphconv_regression_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+# DCmodels
+def test_attentivefp_regression_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/attentivefp_regression_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+def test_pytorchmpnn_regression_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'model_json/pytorchmpnn_regression_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    saved_model_identity(pparams)
+
+
+if __name__ == "__main__":
+    # ------ random forest 
+    print("test_RF_regression_reproducibility")
+    test_RF_regression_reproducibility()
+    print("test_RF_regression_kfold_reproducibility")
+    test_RF_regression_kfold_cv_reproducibility()
+    print("test_RF_classification_reproducibility")
+    test_RF_classification_reproducibility()
+    print("test_RF_classification_kfold_reproducibility")
+    test_RF_classification_kfold_cv_reproducibility()
+
+    # ------ neural network
+    print("test_NN_regression_reproducibility")
+    test_NN_regression_reproducibility()
+    print("test_NN_regression_kfold_reproducibility")
+    test_NN_regression_kfold_cv_reproducibility()
+    print("test_NN_classification_reproducibility")
+    test_NN_classification_reproducibility()
+    print("test_NN_classification_kfold_reproducibility")
+    test_NN_classification_kfold_cv_reproducibility()
+
+    # ------ xgboost
+    print("test_xgboost_regression_reproducibility")
+    test_xgboost_regression_reproducibility()
+    print("test_xgboost_regression_kfold_reproducibility")
+    test_xgboost_regression_kfold_cv_reproducibility()
+    print("test_xgboost_classification_reproducibility")
+    test_xgboost_classification_reproducibility()
+    print("test_xgboost_classification_kfold_reproducibility")
+    test_xgboost_classification_kfold_cv_reproducibility()
+
+    # ------ graphconv
+    print("test_graphconv_classification_reproducibility")
+    test_graphconv_classification_reproducibility()
+    print("test_graphconv_regression_reproducibility")
+    test_graphconv_regression_reproducibility()
+
+    # ------ dcmodels
+    print("test_attentivefp_regression_reproducibility")
+    test_attentivefp_regression_reproducibility()
+
+    print("test_pytorchmpnn_regression_reproducibility")
+    test_pytorchmpnn_regression_reproducibility()
+
+    print("Passed!")
+    
\ No newline at end of file
diff --git a/atomsci/ddm/test/integrative/seed_test/test_seed_splitting.py b/atomsci/ddm/test/integrative/seed_test/test_seed_splitting.py
new file mode 100644
index 00000000..5feea923
--- /dev/null
+++ b/atomsci/ddm/test/integrative/seed_test/test_seed_splitting.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python 
+"""Testing the reproducibility of seeding a random seed in AMPL splitters to recreate split datasets."""
+
+import pandas as pd 
+import copy
+import json 
+import os
+
+from atomsci.ddm.pipeline import model_pipeline as mp
+from atomsci.ddm.pipeline import parameter_parser as parse 
+
+import sys
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+from integrative_utilities import extract_seed, modify_params_with_seed
+
+#----------------------------------------------------------------------------------------------------------
+def split_dataset(pparams):
+    model_pipe = mp.ModelPipeline(pparams)
+    split_uuid = model_pipe.split_dataset()
+    pparams.split_uuid = split_uuid
+    return pparams
+
+def compare_splits(original_split_csv, retrained_split_csv):
+    original_split = pd.read_csv(original_split_csv)
+    retrained_split = pd.read_csv(retrained_split_csv)
+
+    comparison_df = original_split.merge(retrained_split, on='cmpd_id', suffixes=('_original', '_retrained'))
+    
+    # Initialize a variable to track if all comparisons are valid
+    all_match = True
+
+    # Iterate through rows to compare the 'subset' and 'fold' columns
+    for index, row in comparison_df.iterrows():
+        subset_match = (row['subset_original'] == row['subset_retrained'])
+        fold_match = (row['fold_original'] == row['fold_retrained'])
+        
+        if not (subset_match and fold_match):
+            print(f"Mismatch found for cmpd_id {row['cmpd_id']}: "
+                  f"original subset = {row['subset_original']}, "
+                  f"retrained subset = {row['subset_retrained']}, "
+                  f"original fold = {row['fold_original']}, "
+                  f"retrained fold = {row['fold_retrained']}")
+            all_match = False
+
+    return all_match
+
+def perform_splits_and_compare(pparams):
+    starting_pparams=split_dataset(pparams)
+    # original split
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    dataset_path = os.path.join(script_path, '../../test_datasets/')
+    if starting_pparams.split_strategy == 'k_fold_cv':
+        original_split_csv = os.path.join(dataset_path, f"{starting_pparams.dataset_name}_{starting_pparams.num_folds}_fold_cv_{starting_pparams.splitter}_{starting_pparams.split_uuid}.csv")
+    else:
+        original_split_csv = os.path.join(dataset_path, f"{starting_pparams.dataset_name}_{starting_pparams.split_strategy}_{starting_pparams.splitter}_{starting_pparams.split_uuid}.csv")
+
+    # extract the seed 
+    metadata_path = os.path.join(starting_pparams.output_dir, 'split_metadata.json')
+    seed = extract_seed(metadata_path)
+
+    # Retrain split with the same seed
+    retrain_pparams = copy.copy(pparams)
+    retrain_pparams.split_uuid = None
+    retrain_pparams.seed = seed
+    
+    retrain_pparams = split_dataset(retrain_pparams)
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    dataset_path = os.path.join(script_path, '../../test_datasets/')
+    if starting_pparams.split_strategy == 'k_fold_cv':
+        retrained_split_csv = os.path.join(dataset_path, f"{retrain_pparams.dataset_name}_{retrain_pparams.num_folds}_fold_cv_{retrain_pparams.splitter}_{retrain_pparams.split_uuid}.csv")
+    else: 
+        retrained_split_csv = os.path.join(dataset_path, f"{retrain_pparams.dataset_name}_{retrain_pparams.split_strategy}_{retrain_pparams.splitter}_{retrain_pparams.split_uuid}.csv")
+
+    # Compare splits
+    
+    splits_match = compare_splits(original_split_csv, retrained_split_csv)
+
+    if splits_match is True:
+        print("The splits match exactly!")
+    else:
+        print("The splits do not match.")
+
+    return splits_match
+
+#----------------------------------------------------------------------------------------------------------
+
+def test_random_train_valid_test_split_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'split_json/test_random_train_valid_test_split.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    perform_splits_and_compare(pparams)
+
+def test_scaffold_train_valid_test_split_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'split_json/test_scaffold_train_valid_test_split.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    perform_splits_and_compare(pparams)
+
+def test_fingerprint_train_valid_test_split_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'split_json/test_fingerprint_train_valid_test.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    perform_splits_and_compare(pparams)
+
+def test_kfold_random_split_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'split_json/test_kfold_random_split.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    perform_splits_and_compare(pparams)
+
+def test_kfold_scaffold_split_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'split_json/test_kfold_scaffold_split.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    perform_splits_and_compare(pparams)
+
+def test_kfold_fingerprint_split_reproducibility():
+    script_path = os.path.dirname(os.path.realpath(__file__))
+    json_file =  os.path.join(script_path, 'split_json/test_kfold_fingerprint_split.json')
+
+    pparams = parse.wrapper(['--config_file', json_file])
+    pparams.dataset_key = os.path.join(script_path, '../../test_datasets/aurka_chembl_base_smiles_union.csv')
+    pparams.result_dir=script_path
+
+    perform_splits_and_compare(pparams)
+
+#----------------------------------------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    print("test_random_train_valid_test_split_reproducibility")
+    test_random_train_valid_test_split_reproducibility()
+
+    print("test_scaffold_train_valid_test_split_reproducibility")
+    test_scaffold_train_valid_test_split_reproducibility()
+
+    print("test_fingerprint_train_valid_test_split_reproducibility")
+    test_fingerprint_train_valid_test_split_reproducibility()
+
+    print("test_kfold_random_split_reproducibility")
+    test_kfold_random_split_reproducibility()
+
+    print("test_kfold_scaffold_split_reproducibility")
+    test_kfold_scaffold_split_reproducibility()
+
+    print("test_kfold_fingerprint_split_reproducibility")
+    test_kfold_fingerprint_split_reproducibility()
+
+    print("Passed!")
\ No newline at end of file
diff --git a/atomsci/ddm/utils/model_file_reader.py b/atomsci/ddm/utils/model_file_reader.py
index 308c21de..1cfb737e 100644
--- a/atomsci/ddm/utils/model_file_reader.py
+++ b/atomsci/ddm/utils/model_file_reader.py
@@ -194,7 +194,14 @@ def get_response_cols(self):
 
         """
         return self.get_training_dataset().get('response_cols')
-    
+
+    def get_random_seed(self):
+        """Returns:
+            (int): random seed used in model training. Returns None if not found.
+
+        """
+        return self.metadata_dict.get('seed')
+
     def get_model_info(self):
         """Extract the model metadata (and if applicable, model metrics)
 
diff --git a/atomsci/ddm/utils/model_retrain.py b/atomsci/ddm/utils/model_retrain.py
index 1e376f5e..bb77aa3f 100644
--- a/atomsci/ddm/utils/model_retrain.py
+++ b/atomsci/ddm/utils/model_retrain.py
@@ -49,7 +49,7 @@
     mlmt_supported = False
 
 
-def train_model(input, output, dskey='', production=False):
+def train_model(input, output, dskey='', production=False, keep_seed=False):
     """Retrain a model saved in a model_metadata.json file
     
     Args:
@@ -76,6 +76,13 @@ def train_model(input, output, dskey='', production=False):
 
     # Parse parameters
     params = parse.wrapper(config)
+
+     # keep or discard seed.
+    if keep_seed and params.seed is None:
+        raise RuntimeWarning("Expected to find random seed not found. Retraining using a new random seed.")
+    elif not keep_seed:
+        params.seed = None
+
     params.result_dir = output
     # otherwise this will have the same uuid as the source model
     params.model_uuid = None
@@ -101,7 +108,7 @@ def train_model(input, output, dskey='', production=False):
 
     return model
 
-def train_model_from_tar(input, output, dskey='', production=False):
+def train_model_from_tar(input, output, dskey='', production=False, keep_seed=False):
     """Retrain a model saved in a tar.gz file
     
     Args:
@@ -111,6 +118,8 @@ def train_model_from_tar(input, output, dskey='', production=False):
 
        dskey (str): new dataset key if file location has changed
 
+       keep_seed (bool): True to keep the same random seed.
+
     Returns:
        the model pipeline object with trained model
     """
@@ -122,9 +131,9 @@ def train_model_from_tar(input, output, dskey='', production=False):
     # make metadata path
     metadata_path = os.path.join(tmpdir, 'model_metadata.json')
     
-    return train_model(metadata_path, output, dskey=dskey, production=production)
+    return train_model(metadata_path, output, dskey=dskey, production=production, keep_seed=keep_seed)
 
-def train_model_from_tracker(model_uuid, output_dir, production=False):
+def train_model_from_tracker(model_uuid, output_dir, production=False, keep_seed=False):
     """Retrain a model saved in the model tracker, but save it to output_dir and don't insert it into the model tracker
 
     Args:
@@ -158,6 +167,12 @@ def train_model_from_tracker(model_uuid, output_dir, production=False):
     #if config[]
     # Parse parameters
     params = parse.wrapper(config)
+    # keep or discard seed.
+    if keep_seed and params.seed is None:
+        raise RuntimeWarning("Expected to find random seed not found. Retraining using a new random seed.")
+    elif not keep_seed:
+        params.seed = None
+
     params.result_dir = output_dir
     # otherwise this will have the same uuid as the source model
     params.model_uuid = None
@@ -181,7 +196,7 @@ def train_model_from_tracker(model_uuid, output_dir, production=False):
 
     return model
 
-def train_models_from_dataset_keys(input, output, pred_type='regression', production=False):
+def train_models_from_dataset_keys(input, output, pred_type='regression', production=False, keep_seed=False):
     """Retrain a list of models from an input file
 
     Args:
@@ -243,7 +258,7 @@ def train_models_from_dataset_keys(input, output, pred_type='regression', produc
         for model_uuid in best_mods.model_uuid.sort_values():
             try:
                 logger.debug('Training %s in %s' % (model_uuid, output))
-                train_model_from_tracker(model_uuid, output, production=production)
+                train_model_from_tracker(model_uuid, output, production=production, keep_seed=keep_seed)
             except Exception:
                 Exception(f'Error for model_uuid {model_uuid}')
                 pass
@@ -263,6 +278,7 @@ def main(argv):
     parser.add_argument('-dk', '--dataset_key', default='', help='Sometimes dataset keys get moved. Specify new location of dataset. Only works when passing in one model at time.')
     parser.add_argument('-pd_type', '--pred_type', default='regression', help='Specify the prediction type used for model retrain. The default is set to regression.')
     parser.add_argument('-prod', '--production', action='store_true', default=False, help='Retrain the model in production mode')
+    parser.add_argument('-keep_seed', '--keep_seed', action='store_true', default=False, help='Retrain the model using the saved seed if available.')
 
     args = parser.parse_args()
 
@@ -277,19 +293,19 @@ def main(argv):
     if os.path.isdir(input):
         # loop
         for path in Path(input).rglob('model_metadata.json'):
-            train_model(path.absolute(), output, production=args.production)
+            train_model(path.absolute(), output, production=args.production, keep_seed=args.keep_seed)
     elif os.path.isfile(input):
         # 2 if it's a file, check if it's a json or tar.gz or file that contains list of dataset keys
         if input.endswith('.json'):
-            train_model(input, output, dskey=args.dataset_key, production=args.production)
+            train_model(input, output, dskey=args.dataset_key, production=args.production, keep_seed=args.keep_seed)
         elif input.endswith('.tar.gz'):
-            train_model_from_tar(input, output, dskey=args.dataset_key, production=args.production)
+            train_model_from_tar(input, output, dskey=args.dataset_key, production=args.production, keep_seed=args.keep_seed)
         else:
-            train_models_from_dataset_keys(input, output, pred_type=args.pred_type, production=args.production)
+            train_models_from_dataset_keys(input, output, pred_type=args.pred_type, production=args.production, keep_seed=args.keep_seed)
     else:
         try:
             # 3 try to process 'input' as uuid
-            train_model_from_tracker(input, output, production=args.production)
+            train_model_from_tracker(input, output, production=args.production, keep_seed=args.keep_seed)
         except Exception:
             Exception('Unrecognized input %s'%input)
 
diff --git a/pip/cpu_requirements.txt b/pip/cpu_requirements.txt
index f3feb350..00afdfa3 100644
--- a/pip/cpu_requirements.txt
+++ b/pip/cpu_requirements.txt
@@ -29,6 +29,8 @@ pyarrow
 
 bravado
 
+imblearn
+
 # optional for home users: prettier images in RDKit
 # requires pkg-config to build: sudo apt-get pkg-config
 # requires Cairo: sudo apt-get install libcairo2-dev
diff --git a/pip/cuda_requirements.txt b/pip/cuda_requirements.txt
index c223ad16..3b16ac75 100644
--- a/pip/cuda_requirements.txt
+++ b/pip/cuda_requirements.txt
@@ -31,6 +31,8 @@ pyarrow
 
 bravado
 
+imblearn
+
 # optional for home users: prettier images in RDKit
 # requires pkg-config to build: sudo apt-get pkg-config
 # requires Cairo: sudo apt-get install libcairo2-dev
diff --git a/pip/mchip_requirements.txt b/pip/mchip_requirements.txt
index 6bb58ff1..cf005cd4 100644
--- a/pip/mchip_requirements.txt
+++ b/pip/mchip_requirements.txt
@@ -30,6 +30,8 @@ pyarrow
 
 bravado
 
+imblearn
+
 # optional for home users: prettier images in RDKit
 # requires pkg-config to build: sudo apt-get pkg-config
 # requires Cairo: sudo apt-get install libcairo2-dev
diff --git a/pip/rocm_requirements.txt b/pip/rocm_requirements.txt
index 296f7b70..08601156 100644
--- a/pip/rocm_requirements.txt
+++ b/pip/rocm_requirements.txt
@@ -19,6 +19,8 @@ umap-learn
 
 pyarrow
 
+imblearn
+
 # requires pkg-config to build: sudo apt-get pkg-config
 # requires Cairo: sudo apt-get install libcairo2-dev
 # pycairo