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:

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(
    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

# 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

# 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:

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:

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:

import deepchem as dc
1. Load data (pretrained models often need raw SMILES)

loader = dc.data.CSVLoader(
    tasks=['activity'],
    feature_field='smiles',
    featurizer=dc.feat.DummyFeaturizer()  # Model handles featurization
)
dataset = loader.create_dataset('small_dataset.csv')
2. Split data

splitter = dc.splits.ScaffoldSplitter()
train, test = splitter.train_test_split(dataset)
3. Load pretrained model

model = dc.models.HuggingFaceModel(
    model='seyonec/ChemBERTa-zinc-base-v1',
    task='classification',
    n_tasks=1,
    learning_rate=2e-5
)
4. Fine-tune

model.fit(train, nb_epoch=10)
5. Evaluate

predictions = model.predict(test)

See references/workflows.md for 8 detailed workflow examples covering molecular generation, materials science, protein analysis, and more.

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.

# 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.

# 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) on molecular property prediction tasks.

# 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

# 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

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

Start with Random Forest + CircularFingerprint (fast baseline)

Try XGBoost/LightGBM if RF works well

Move to deep learning (MultitaskRegressor) if you have >5K samples

Try GNNs if you have >10K samples

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: Module not found errors.
Solution: Ensure DeepChem is installed with required dependencies:

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:

Molecular property prediction from SMILES

Using MoleculeNet benchmarks

Hyperparameter optimization

Transfer learning with pretrained models

Molecular generation with GANs

Materials property prediction

Protein sequence analysis

Custom model integration

When to reference: Use these workflows as templates for implementing complete solutions.

Installation Notes

Basic installation:

uv pip install deepchem

For PyTorch models (GCN, GAT, etc.):

uv pip install deepchem[torch]

For all features:

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"

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