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.

Version note: Examples target deepchem 2.8.0 (PyPI stable, Apr 2024). Requires Python 3.7–3.11 (`5K samples

1. Try GNNs if you have >10K samples
2. Use transfer learning for small datasets or novel scaffolds

Pattern 4: Handle Imbalanced Data

# 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

# 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: No module named 'torch' / No module named 'tensorflow' warnings, or model classes fail to import. Solution: DeepChem loads lazily — install the backend that matches your model, then add the matching extra:

uv pip install deepchem              # loaders, featurizers, MoleculeNet only
uv pip install 'deepchem[torch]'       # GCN, GAT, AttentiveFP, HuggingFaceModel, GroverModel
uv pip install 'deepchem[tensorflow]'  # legacy Keras models
uv pip install 'deepchem[jax]'         # Haiku/JAX models

Install PyTorch or TensorFlow with the correct CUDA build before the extra when using GPUs. Quote extras in zsh: 'deepchem[torch]'.

Conda + PyTorch users: If import deepchem fails with undefined symbol: iJIT_NotifyEvent, pin MKL below 2025 (conda install "mkl<2025") — PyTorch wheels may be incompatible with MKL 2025.0.0.

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

Core package (data loaders, featurizers, MoleculeNet, scikit-learn wrappers):

uv pip install deepchem

Add the extra that matches your model backend (install PyTorch/TensorFlow/JAX first for GPU builds):

uv pip install 'deepchem[torch]'       # GNNs, TorchModel, HuggingFaceModel, GroverModel
uv pip install 'deepchem[tensorflow]'  # Keras/TensorFlow models
uv pip install 'deepchem[jax]'         # JAX/Haiku models
uv pip install 'deepchem[dqc]'         # Differentiable quantum chemistry (torch + xitorch)

Nightly builds: uv pip install --pre deepchem (same extras apply with --pre).

See installation guide and soft requirements for optional dependencies per model class.

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"

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Tyche-MKR
• Source: Tyche-MKR/scientific-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.