DeepChem

When to Use This Skill

This skill should be used when:

• Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
• Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
• Training models on chemical/biological datasets
• Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
• Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)
• Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)
• Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)
• Predicting crystal/materials properties (bandgap, formation energy)
• Analyzing protein or DNA sequences

1. Molecular Data Loading and Processing

DeepChem provides specialized loaders for various chemical data formats:

import deepchem as dc

## Load SDF files

loader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer)
dataset = loader.create_dataset('compounds.sdf')