DeepChem — Deep Learning for Drug Discovery

Overview

DeepChem is an open-source Python framework providing a unified API for molecular machine learning across drug discovery, materials science, and quantum chemistry. It wraps 60+ model architectures (graph neural networks, transformers, classical ML) with 50+ molecular featurizers and standardized datasets (MoleculeNet), enabling end-to-end workflows from SMILES strings to trained predictive models.

When to Use

• Predicting molecular properties (solubility, toxicity, binding affinity) from SMILES
• Benchmarking models on MoleculeNet standardized datasets (BBBP, Tox21, ESOL, FreeSolv, etc.)
• Training graph neural networks on molecular graphs (GCN, GAT, AttentiveFP, MPNN, DMPNN)
• Fine-tuning pretrained chemical language models (ChemBERTa, GROVER, MolFormer)
• Running hyperparameter optimization for molecular ML models
• Virtual screening and hit prioritization with trained models
• Materials property prediction from crystal structures (CGCNN, MEGNet)
• Protein-ligand interaction modeling and binding affinity prediction
• For fingerprint-based cheminformatics without deep learning, use rdkit-cheminformatics instead
• For featurization only (no model training), use molfeat-molecular-featurization instead