DeepChem

Deep learning for the life sciences: drug discovery, quantum chemistry, materials science, bioinformatics.

When to Use This Skill

• Building ML models on molecular datasets (SMILES, graphs, fingerprints)
• Working with MoleculeNet benchmark datasets
• Predicting molecular properties (solubility, toxicity, binding affinity)
• Protein-ligand interaction modeling
• Quantum chemistry property prediction (QM9, GDB datasets)
• Featurizing molecules for downstream ML tasks
• Virtual screening and drug discovery pipelines

Quick Start — Standard Workflow

import deepchem as dc

# 1. Load dataset with featurizer
tasks, datasets, transformers = dc.molnet.load_delaney(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = datasets

# 2. Create model
model = dc.models.GraphConvModel(n_tasks=1, mode='regression', dropout=0.2)

# 3. Train
model.fit(train_dataset, nb_epoch=100)

# 4. Evaluate
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)
train_score = model.evaluate(train_dataset, [metric], transformers)
test_score  = model.evaluate(test_dataset,  [metric], transformers)

# 5. Predict
predictions = model.predict_on_batch(test_dataset.X[:10])

Router — What to Read

Installation

pip install --pre deepchem          # with TensorFlow
pip install --pre deepchem[torch]   # with PyTorch
pip install --pre deepchem[jax]     # with JAX

import deepchem as dc
dc.__version__   # verify installation

Key Submodules

Related Skills

• rdkit-patterns - Molecular manipulation before DeepChem ingestion
• cheminformatics - SMILES, molecular representations reference