Prediction of Aqueous Solubility of Drug Molecules

Overview

Predicting the aqueous solubility of small organic molecules is a critical step in early-stage drug discovery, influencing a compound's absorption, distribution, metabolism, and excretion (ADME) properties. This project explores and compares two primary computational approaches for classifying molecular solubility:

1. Descriptor-based Quantitative Structure-Property Relationship (QSPR) Modeling: Utilizes molecular descriptors generated by RDKit with a LightGBM classifier.
2. Graph Convolutional Neural Networks (GCNs): Employs graph representations of molecules with PyTorch Geometric to learn predictive features.

Data Source

• Aqueous solubility dataset (containing data for approximately 9,000 unique molecules): Published in Nature Scientific Data

Model Highlights

Example molecules from the dataset.

Visualization from GCN hyperparameter tuning.

Dependencies

• PyTorch
• PyTorch-Geometric
• RDKit
• DeepChem
• Scikit-Learn
• LightGBM
• Pandas
• NumPy
• Seaborn
• Matplotlib
• tqdm
• Wandb (for hyperparameters search.ipynb)

Repository Structure

• analytics.ipynb: Data exploration, feature analysis, and visualization.
• descriptor-based-model.ipynb: Implementation and evaluation of the QSPR model using molecular descriptors and LightGBM.
• hyperparameters search.ipynb: Hyperparameter optimization for the GCN model using Weights & Biases.
• model training and evaluation.ipynb: Training, evaluation, and analysis of the GCN model.
• custom_dataset.py: Defines the custom PyTorch Geometric dataset for loading molecular graphs.
• model.py: Contains the GCN model architecture definition.
• trainer.py: Implements the training and validation loop for the GCN model.
• utils.py: Utility functions used across notebooks (e.g., for evaluation metrics, plotting).
• data/: Directory containing the dataset files.

Read a Summary

Want to read a more readable version of this project? You can find it here: Prediction of Aqueous Solubility