SynVerse

Table of Contents

• Introduction
• Conda Environment Setup
• Download Processed Dataset
• How to Use SynVerse
  • Configuration File
• Docker Setup Guide for Pretrained Models
  • Prerequisites
  • Build the Docker Image
• Publication

Introduction

SynVerse is a framework with an encoder-decoder architecture. It incorporates diverse input features and a reasonable approximation of model architectures commonly employed by existing deep learning-based synergy prediction methods. It includes four data-splitting strategies and three ablation methods: module-based, feature shuffling, and a novel network-based approach to isolate factors influencing model performance.

Conda Environment Setup

If you haven't cloned the repository yet, run the following command to clone it and navigate to the SynVerse folder:

git clone https://github.com/Murali-group/SynVerse.git
cd SynVerse

Then, follow the steps below to set up the synverse environment with required libraries using the provided synverse.yml file.

conda env create -f synverse.yml

To run the command, make sure Conda is installed. If not, install Anaconda or the lighter version, Miniconda.

After the environment is created, activate it using:

conda activate synverse

To verify that the environment and its dependencies are set up correctly, you can list the installed packages:

conda list

Download Processed Dataset

All datasets used in this study, including drug and cell line features and the preprocessed synergy dataset required to reproduce the results, are available in the Zenodo repository. Download and unzip the inputs.zip file, and place the inputs folder in the project directory, at the same level as the code/ folder.

How to Use SynVerse

SynVerse is configured using a YAML file (e.g., sample_config.yaml), which allows users to define the input features, model architecture, and evaluation strategies. Once a configuration file is prepared, SynVerse can be run in various modes to perform different tasks:

1. To train and test a model:

   python main.py --config config_files/sample_config.yaml --train_type 'regular'

2. To perform feature-shuffling-based ablation study:

   python main.py --config config_files/sample_config.yaml --train_type 'shuffle'

3. To perform network-based ablation study:

   python main.py --config config_files/sample_config.yaml --train_type 'rewire'

4. To parse the output files and create plots showing RMSE and PCC score of the models:

    python -m code.plots.results_plots --parse --config code/config_files/sample_config.yaml
    python -m code.plots.results_plots --plot --config code/config_files/sample_config.yaml 

Configuration File

This section describes each field in the YAML configuration file used by SynVerse.

score_name

The synergy score to predict (Options:'S_mean_mean', 'synergy_loewe_mean')

input_dir

Base directory where all input files are stored.

input_files

Each entry defines a path to a required input file.

Key	Description
synergy_file	Contains synergy triplets. Required columns: drug_1_pid, drug_2_pid, cell_line_name, and S_mean_mean (or synergy_loewe_mean).
maccs_file	MACCS fingerprint file. Columns: pid, MACCS_0, ..., MACCS_166.
mfp_file	Morgan fingerprints. Columns: pid, Morgan_FP_0, ..., Morgan_FP_255.
ecfp_file	ECFP_4 fingerprints. Columns: pid, ECFP4_0, ..., ECFP4_1023.
smiles_file	SMILES strings. Columns: pid, smiles.
mol_graph_file	Pickle file with DeepChem-derived molecular graphs: {pid: graph}.
target_file	Drug target binary profile. Columns: pid and target names
genex_file	Cell line gene expression. Columns: cell_line_name and gene names.
lincs	Landmark genes file for LINCS1000.
vocab_file	Vocabulary file to convert smiles to tokens.
net_file	STRING network file (gzipped).
prot_info_file	STRING protein metadata file (gzipped).

---

drug_features

Describes drug-level features.

Features appearing here will determine which subset of triplets from synergy_file is used in training, validation, and test. Example 1: If only SMILES-based features such as MACCS, MFP, ECFP_4, mol_graph, smiles appear in drug_features, then the final synergy dataset will contain the triplets where both drugs have SMILES available. Example 2: If both SMILES-based features such as MACCS, MFP, ECFP_4, mol_graph, smiles, and target appear in the list of drug_features, then the final synergy dataset will contain the triplets where drugs have both smiles and target data available.

• name: str: Feature name
• preprocess: str: Preprocessing method
• compress: bool: Use autoencoder to reduce dimensions.
• norm: str: Normalization method
• encoder: str: Feature-specific encoders
• use: List of boolean values: Determines if the feature should be used in the model.

cell_line_features

Same structure as drug_features, but for cell lines.

model_info

decoder

• name: Model architecture (e.g., 'MLP').
• hp_range: Hyperparameter search space for tuning.
• hp: Default configuration.

drug_encoder

List of encoder configs. Each contains:

• name: Encoder type (e.g., 'GCN', 'Transformer').
• hp_range: Hyperparameter search space for tuning.
• hp: Default configuration.

autoencoder_dims

Dimension of hidden layers for the autoencoder.

batch_size

Number of samples per batch during training.

epochs

Maximum training epochs.

splits

• type: Spitting strategy to use (Options: random, leave_comb, leave_drug, leave_cell_line).
• test_frac: Test set size (fraction of total).
• val_frac: Validation set size (fraction of training set).

wandb

Weights & Biases integration for experiment tracking.

• enabled: Enable logging.
• entity_name, token, project_name: W&B credentials.
• timezone, timezone_format: Time info formatting.

abundance

Minimum percentage of triplets required per cell line to be included in training.

Feature Combination Control

Defines the number of features in combinations.

Field	Meaning
max_drug_feat	Maximum number of drug features per model.
min_drug_feat	Minimum number of drug features per model.
max_cell_feat	Maximum number of cell line features per model.
min_cell_feat	Minimum number of cell line features per model.

hp_tune

Set to true to enable hyperparameter optimization.

bohb

Settings for Bayesian Optimization with BOHB.

• min_budget, max_budget: Resource limits per trial.
• n_iterations: Number of trials.
• run_id: BOHB session ID.
• server_type: 'local' or 'cluster'.

rewire_method

Which network rewiring method to use when flag --train_type = rewire. Options: SM: Degree-preserving (Maslov-Sneppen), SA: Strength-preserving (Simulated Annealing).

output_settings

• output_dir: Output directory for results.

Docker Setup Guide for Pretrained Models

This guide provides detailed instructions for containerizing KPGT and MolE using Docker with GPU support.

Prerequisites

• Hardware Requirements

  • NVIDIA GPU with Compute Capability ≥ 3.5
  • Minimum 16GB+ RAM
  • 50GB+ free disk space for Docker images and dependencies

• Software Requirements

  • For KPGT (CUDA 11.3)
    • OS: Linux (Ubuntu 20.04 recommended)
    • NVIDIA drivers: v465+ (CUDA 11.3 compatibility)
    • CUDA Toolkit: 11.3.1
    • Docker: Engine 20.10+ with NVIDIA Container Toolkit configured for CUDA 11.3
  • For MolE (CUDA 12.1)
    • OS: Linux (Ubuntu 20.04 recommended)
    • NVIDIA drivers: v525+ (CUDA 12.1 compatibility)
    • CUDA Toolkit: 12.1
    • Docker: Engine 20.10+ with NVIDIA Container Toolkit configured for CUDA 12.1

Build the Docker Image

We already have created Dockerfiles for each project. To build the Docker images, simply run the provided script. This script will build the kpgt:base image and the mole:base image using their respective Dockerfiles located in the root directory of each project at pretrain directory.

Publication

Tasnina, N., Haghani, M. and Murali, T.M., 2025. SynVerse: A Framework for Systematic Evaluation of Deep Learning Based Drug Synergy Prediction Models. bioRxiv, pp.2025-04. https://doi.org/10.1101/2025.04.30.651516