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:

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

# 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 (<1K): CircularFingerprint or RDKitDescriptors
• Medium datasets (1K-100K): CircularFingerprint or graph featurizers
• Large 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.

# 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

Example: Traditional ML

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

# 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

# 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:

# 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:

# ChemBERTa (BERT pretrained on 77M molecules)
model = dc.models.HuggingFaceModel(
how to use deepchem
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use deepchem on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add deepchem
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill deepchemcopy
The skills CLI fetches deepchem from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/deepchem
Reload or restart Cursor to activate deepchem. Access the skill through slash commands (e.g., /deepchem) or your agent's skill management interface.
⚠
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