deepchem
Use when working with DeepChem for molecular machine learning, drug discovery, quantum chemistry, materials science, or bioinformatics. Handles molecular datasets, featurization strategies, model training/evaluation, and predictions on chemical data.
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
| Task | Reference |
|---|---|
| Dataset creation, access, splitters | references/core-concepts.md |
| Training workflow, metrics, hyperopt, multitask | references/model-training.md |
| Fingerprints, GCN, ChemBERTa, graph models | references/mol-machine-learning.md |
| MoleculeNet, protein-ligand, virtual screening | references/drug-discovery.md |
| QM9, DeepQMC, materials science | references/quantum-materials.md |
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
| Submodule | Role |
|---|---|
dc.molnet | MoleculeNet dataset loaders |
dc.models | All model classes |
dc.feat | Featurizers |
dc.metrics | Evaluation metrics |
dc.splits | Dataset splitters |
dc.data | Dataset classes |
dc.trans | Transformers (normalization, etc.) |
Related Skills
rdkit-patterns- Molecular manipulation before DeepChem ingestioncheminformatics- SMILES, molecular representations reference