Deepchem

# DeepChem ## Overview DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models. ## 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 ## Core Capabilities ### 1. Molecular Data Loading and Processing DeepChem provides specialized loaders for various chemical data formats: ```python import deepchem as dc # Load CSV with SMILES featurizer = dc.feat.CircularFingerprint(radius=2, size=2048) loader = dc.data.CSVLoader( tasks=['solubility', 'toxicity'], feature_field='smiles', featurizer=featurizer ) dataset = loader.create_dataset('molecules.csv') # Load SDF files loader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer) dataset = loader.create_dataset('compounds.sdf') # Load protein sequences loader = dc.data.FASTALoader() dataset = loader.create_dataset('proteins.fasta') ``` **Key Loaders**: - `CSVLoader`: Tabular data with molecular identifiers - `SDFLoader`: Molecular structure files - `FASTALoader`: Protein/DNA sequences - `ImageLoader`: Molecular images - `JsonLoader`: JSON-formatted datasets ### 2. Molecular Featurization Convert molecules into numerical representations for ML models. #### Decision Tree for Featurizer Selection ``` Is the model a graph neural network? ├─ YES → Use graph featurizers │ ├─ Standard GNN → MolGraphConvFeaturizer │ ├─ Message passing → DMPNNFeaturizer │ └─ Pretrained → GroverFeaturizer │ └─ NO → What type of model? ├─ Traditional ML (RF, XGBoost, SVM) │ ├─ Fast baseline → CircularFingerprint (ECFP) │ ├─ Interpretable → RDKitDescriptors │ └─ Maximum coverage → MordredDescriptors │ ├─ Deep learning (non-graph) │ ├─ Dense networks → CircularFingerprint │ └─ CNN → SmilesToImage │ ├─ Sequence models (LSTM, Transformer) │ └─ SmilesToSeq │ └─ 3D structure analysis └─ CoulombMatrix ``` #### Example Featurization ```python # Fingerprints (for traditional ML) fp = dc.feat.CircularFingerprint(radius=2, size=2048) # Descriptors (for interpretable models) desc = dc.feat.RDKitDescriptors() # Graph features (for GNNs) graph_feat = dc.feat.MolGraphConvFeaturizer() # Apply featurization features = fp.featurize(['CCO', 'c1ccccc1']) ``` **Selection Guide**: - **Small datasets (100K)**: Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer) - **Transfer learning**: Pretrained model featurizers (GroverFeaturizer) See `references/api_reference.md` for complete featurizer documentation. ### 3. Data Splitting **Critical**: For drug discovery tasks, use `ScaffoldSplitter` to prevent data leakage from similar molecular structures appearing in both training and test sets. ```python # Scaffold splitting (recommended for molecules) splitter = dc.splits.ScaffoldSplitter() train, valid, test = splitter.train_valid_test_split( dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1 ) # Random splitting (for non-molecular data) splitter = dc.splits.RandomSplitter() train, test = splitter.train_test_split(dataset) # Stratified splitting (for imbalanced classification) splitter = dc.splits.RandomStratifiedSplitter() train, test = splitter.train_test_split(dataset) ``` **Available Splitters**: - `ScaffoldSplitter`: Split by molecular scaffolds (prevents leakage) - `ButinaSplitter`: Clustering-based molecular splitting - `MaxMinSplitter`: Maximize diversity between sets - `RandomSplitter`: Random splitting - `RandomStratifiedSplitter`: Preserves class distributions ### 4. Model Selection and Training #### Quick Model Selection Guide | Dataset Size | Task | Recommended Model | Featurizer | |-------------|------|-------------------|------------| | 100K | Molecular properties | GCNModel, AttentiveFPModel, DMPNNModel | MolGraphConvFeaturizer | | Any (small preferred) | Transfer learning | ChemBERTa, GROVER, MolFormer | Model-specific | | Crystal structures | Materials properties | CGCNNModel, MEGNetModel | Structure-based | | Protein sequences | Protein properties | ProtBERT | Sequence-based | #### Example: Traditional ML ```python from sklearn.ensemble import RandomForestRegressor # Wrap scikit-learn model sklearn_model = RandomForestRegressor(n_estimators=100) model = dc.models.SklearnModel(model=sklearn_model) model.fit(train) ``` #### Example: Deep Learning ```python # Multitask regressor (for fingerprints) model = dc.models.MultitaskRegressor( n_tasks=2, n_features=2048, layer_sizes=[1000, 500], dropouts=0.25, learning_rate=0.001 ) model.fit(train, nb_epoch=50) ``` #### Example: Graph Neural Networks ```python # Graph Convolutional Network model = dc.models.GCNModel( n_tasks=1, mode='regression', batch_size=128, learning_rate=0.001 ) model.fit(train, nb_epoch=50) # Graph Attention Network model = dc.models.GATModel(n_tasks=1, mode='classification') model.fit(train, nb_epoch=50) # Attentive Fingerprint model = dc.models.AttentiveFPModel(n_tasks=1, mode='regression') model.fit(train, nb_epoch=50) ``` ### 5. MoleculeNet Benchmarks Quick access to 30+ curated benchmark datasets with standardized train/valid/test splits: ```python # Load benchmark dataset tasks, datasets, transformers = dc.molnet.load_tox21( featurizer='GraphConv', # or 'ECFP', 'Weave', 'Raw' splitter='scaffold', # or 'random', 'stratified' reload=False ) train, valid, test = datasets # Train and evaluate model = dc.models.GCNModel(n_tasks=len(tasks), mode='classification') model.fit(train, nb_epoch=50) metric = dc.metrics.Metric(dc.metrics.roc_auc_score) test_score = model.evaluate(test, [metric]) ``` **Common Datasets**: - **Classification**: `load_tox21()`, `load_bbbp()`, `load_hiv()`, `load_clintox()` - **Regression**: `load_delaney()`, `load_freesolv()`, `load_lipo()` - **Quantum properties**: `load_qm7()`, `load_qm8()`, `load_qm9()` - **Materials**: `load_perovskite()`, `load_bandgap()`, `load_mp_formation_energy()` See `references/api_reference.md` for complete dataset list. ### 6. Transfer Learning Leverage pretrained models for improved performance, especially on small datasets: ```python # ChemBERTa (BERT pretrained on 77M molecules) model = dc.models.HuggingFaceModel( model='seyonec/ChemBERTa-zinc-base-v1', task='classification', n_tasks=1, learning_rate=2e-5 # Lower LR for fine-tuning ) model.fit(train, nb_epoch=10) # GROVER (graph transformer pretrained on 10M molecules) model = dc.models.GroverModel( task='regression', n_tasks=1 ) model.fit(train, nb_epoch=20) ``` **When to use transfer learning**: - Small datasets (5K samples 4. Try GNNs if you have >10K samples 5. Use transfer learning for small datasets or novel scaffolds ### Pattern 4: Handle Imbalanced Data ```python # Option 1: Balancing transformer transformer = dc.trans.BalancingTransformer(dataset=train) train = transformer.transform(train) # Option 2: Use balanced metrics metric = dc.metrics.Metric(dc.metrics.balanced_accuracy_score) ``` ### Pattern 5: Avoid Memory Issues ```python # Use DiskDataset for large datasets dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids) # Use smaller batch sizes model = dc.models.GCNModel(batch_size=32) # Instead of 128 ``` ## Common Pitfalls ### Issue 1: Data Leakage in Drug Discovery **Problem**: Using random splitting allows similar molecules in train/test sets. **Solution**: Always use `ScaffoldSplitter` for molecular datasets. ### Issue 2: GNN Underperforming vs Fingerprints **Problem**: Graph neural networks perform worse than simple fingerprints. **Solutions**: - Ensure dataset is large enough (>10K samples typically) - Increase training epochs (50-100) - Try different architectures (AttentiveFP, DMPNN instead of GCN) - Use pretrained models (GROVER) ### Issue 3: Overfitting on Small Datasets **Problem**: Model memorizes training data. **Solutions**: - Use stronger regularization (increase dropout to 0.5) - Use simpler models (Random Forest instead of deep learning) - Apply transfer learning (ChemBERTa, GROVER) - Collect more data ### Issue 4: Import Errors **Problem**: Module not found errors. **Solution**: Ensure DeepChem is installed with required dependencies: ```bash uv pip install deepchem # For PyTorch models uv pip install deepchem[torch] # For all features uv pip install deepchem[all] ``` ## Reference Documentation This skill includes comprehensive reference documentation: ### `references/api_reference.md` Complete API documentation including: - All data loaders and their use cases - Dataset classes and when to use each - Complete featurizer catalog with selection guide - Model catalog organized by category (50+ models) - MoleculeNet dataset descriptions - Metrics and evaluation functions - Common code patterns **When to reference**: Search this file when you need specific API details, parameter names, or want to explore available options. ### `references/workflows.md` Eight detailed end-to-end workflows: 1. Molecular property prediction from SMILES 2. Using MoleculeNet benchmarks 3. Hyperparameter optimization 4. Transfer learning with pretrained models 5. Molecular generation with GANs 6. Materials property prediction 7. Protein sequence analysis 8. Custom model integration **When to reference**: Use these workflows as templates for implementing complete solutions. ## Installation Notes Basic installation: ```bash uv pip install deepchem ``` For PyTorch models (GCN, GAT, etc.): ```bash uv pip install deepchem[torch] ``` For all features: ```bash uv pip install deepchem[all] ``` If import errors occur, the user may need specific dependencies. Check the DeepChem documentation for detailed installation instructions. ## Additional Resources - Official documentation: https://deepchem.readthedocs.io/ - GitHub repository: https://github.com/deepchem/deepchem - Tutorials: https://deepchem.readthedocs.io/en/latest/get_started/tutorials.html - Paper: "MoleculeNet: A Benchmark for Molecular Machine Learning"