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

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

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:

pip install deepchem

For PyTorch models

pip install deepchem[torch]

For all features

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:

pip install deepchem

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

pip install deepchem[torch]

For all features:

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"