deepchem — quality + safety report

---
name: deepchem
description: Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
license: MIT license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.7–3.11 (PyPI 2.8.0 caps at <3.12). Install PyTorch, TensorFlow, or JAX before the matching deepchem extra. RDKit is a core dependency.
metadata:
  version: "1.1"
  skill-author: K-Dense Inc.
---

# 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

### 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 (<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.

```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 |
|-------------|------|-------------------|------------|
| < 1K samples | Any | SklearnModel (RandomForest) | CircularFingerprint |
| 1K-100K | Classification/Regression | GBDTModel or MultitaskRegressor | CircularFingerprint |
| > 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 (< 1000 samples)
- Novel molecular scaffolds
- Limited computational resources
- Need for rapid prototyping

Use the `scripts/transfer_learning.py` script for guided transfer learning workflows.

### 7. Model Evaluation

```python
# Define metrics
classification_metrics = [
    dc.metrics.Metric(dc.metrics.roc_auc_score, name='ROC-AUC'),
    dc.metrics.Metric(dc.metrics.accuracy_score, name='Accuracy'),
    dc.metrics.Metric(dc.metrics.f1_score, name='F1')
]

regression_metrics = [
    dc.metrics.Metric(dc.metrics.r2_score, name='R²'),
    dc.metrics.Metric(dc.metrics.mean_absolute_error, name='MAE'),
    dc.metrics.Metric(dc.metrics.root_mean_squared_error, name='RMSE')
]

# Evaluate
train_scores = model.evaluate(train, classification_metrics)
test_scores = model.evaluate(test, classification_metrics)
```

### 8. Making Predictions

```python
# Predict on test set
predictions = model.predict(test)

# Predict on new molecules
new_smiles = ['CCO', 'c1ccccc1', 'CC(C)O']
new_features = featurizer.featurize(new_smiles)
new_dataset = dc.data.NumpyDataset(X=new_features)

# Apply same transformations as training
for transformer in transformers:
    new_dataset = transformer.transform(new_dataset)

predictions = model.predict(new_dataset)
```

## Typical Workflows

### Workflow A: Quick Benchmark Evaluation

For evaluating a model on standard benchmarks:

```python
import deepchem as dc

# 1. Load benchmark
tasks, datasets, _ = dc.molnet.load_bbbp(
    featurizer='GraphConv',
    splitter='scaffold'
)
train, valid, test = datasets

# 2. Train model
model = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')
model.fit(train, nb_epoch=50)

# 3. Evaluate
metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
test_score = model.evaluate(test, [metric])
print(f"Test ROC-AUC: {test_score}")
```

### Workflow B: Custom Data Prediction

For training on custom molecular datasets:

```python
import deepchem as dc

# 1. Load and featurize data
featurizer = dc.feat.CircularFingerprint(radius=2, size=2048)
loader = dc.data.CSVLoader(
    tasks=['activity'],
    feature_field='smiles',
    featurizer=featurizer
)
dataset = loader.create_dataset('my_molecules.csv')

# 2. Split data (use ScaffoldSplitter for molecules!)
splitter = dc.splits.ScaffoldSplitter()
train, valid, test = splitter.train_valid_test_split(dataset)

# 3. Normalize (optional but recommended)
transformers = [dc.trans.NormalizationTransformer(
    transform_y=True, dataset=train
)]
for transformer in transformers:
    train = transformer.transform(train)
    valid = transformer.transform(valid)
    test = transformer.transform(test)

# 4. Train model
model = dc.models.MultitaskRegressor(
    n_tasks=1,
    n_features=2048,
    layer_sizes=[1000, 500],
    dropouts=0.25
)
model.fit(train, nb_epoch=50)

# 5. Evaluate
metric = dc.metrics.Metric(dc.metrics.r2_score)
test_score = model.evaluate(test, [metric])
```

### Workflow C: Transfer Learning on Small Dataset

For leveraging pretrained models:

```python
import deepchem as dc

# 1. Load data (pretrained models often need raw SMILES)
loader = dc.data.CSVLoader(
    tasks=['activity'],
    feature_fiel

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