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** (`<3.12` on PyPI). Core utilities (loaders, featurizers, MoleculeNet) work without a DL backend; GNN and transformer models need the matching extra (`torch`, `tensorflow`, or `jax`). Install the backend framework first when using GPU builds.

## 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

Eight capability areas, each with worked code, are in
[references/core_capabilities.md](references/core_capabilities.md):

1. **Molecular data loading and processing** — loaders, `NumpyDataset` / `DiskDataset`.
2. **Molecular featurization** — circular fingerprints, graph convolution, and descriptors.
3. **Data splitting** — random, scaffold, stratified, and butina splitters, and why
   scaffold splitting is the honest default for molecules.
4. **Model selection and training** — the model families and how to fit them.
5. **MoleculeNet benchmarks** — loading standard datasets and their published splits.
6. **Transfer learning** — pretraining and fine-tuning.
7. **Model evaluation** — metrics appropriate to regression and classification tasks.
8. **Making predictions** — applying a trained model to new molecules.

Three end-to-end workflows are in
[references/typical_workflows.md](references/typical_workflows.md).

## Example Scripts

This skill includes three production-ready scripts in the `scripts/` directory:

### 1. `predict_solubility.py`
Train and evaluate solubility prediction models. Works with Delaney benchmark or custom CSV data.

```bash
# Use Delaney benchmark
python scripts/predict_solubility.py

# Use custom data
python scripts/predict_solubility.py \
    --data my_data.csv \
    --smiles-col smiles \
    --target-col solubility \
    --predict "CCO" "c1ccccc1"
```

### 2. `graph_neural_network.py`
Train various graph neural network architectures on molecular data.

```bash
# Train GCN on Tox21
python scripts/graph_neural_network.py --model gcn --dataset tox21

# Train AttentiveFP on custom data
python scripts/graph_neural_network.py \
    --model attentivefp \
    --data molecules.csv \
    --task-type regression \
    --targets activity \
    --epochs 100
```

### 3. `transfer_learning.py`
Fine-tune pretrained models (ChemBERTa, GROVER, MolFormer) on molecular property prediction tasks.

```bash
# Fine-tune ChemBERTa on BBBP
python scripts/transfer_learning.py --model chemberta --dataset bbbp

# Fine-tune GROVER on custom data
python scripts/transfer_learning.py \
    --model grover \
    --data small_dataset.csv \
    --target activity \
    --task-type classification \
    --epochs 20
```

## Common Patterns and Best Practices

### Pattern 1: Always Use Scaffold Splitting for Molecules
```python
# GOOD: Prevents data leakage
splitter = dc.splits.ScaffoldSplitter()
train, test = splitter.train_test_split(dataset)

# BAD: Similar molecules in train and test
splitter = dc.splits.RandomSplitter()
train, test = splitter.train_test_split(dataset)
```

### Pattern 2: Normalize Features and Targets
```python
transformers = [
    dc.trans.NormalizationTransformer(
        transform_y=True,  # Also normalize target values
        dataset=train
    )
]
for transformer in transformers:
    train = transformer.transform(train)
    test = transformer.transform(test)
```

### Pattern 3: Start Simple, Then Scale
1. Start with Random Forest + CircularFingerprint (fast baseline)
2. Try XGBoost/LightGBM if RF works well
3. Move to deep learning (MultitaskRegressor) if you have >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)
-