菜单
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
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| name | deepchem |
| description | Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML. |
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.
This skill should be used when:
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()
loader = dc.data.SDFLoader(tasks=[], featurizer=featurizer)
dataset = loader.create_dataset()
loader = dc.data.FASTALoader()
dataset = loader.create_dataset()
Key Loaders:
CSVLoader: Tabular data with molecular identifiersSDFLoader: Molecular structure filesFASTALoader: Protein/DNA sequencesImageLoader: Molecular imagesJsonLoader: JSON-formatted datasetsConvert molecules into numerical representations for ML models.
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
# 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:
See references/api_reference.md for complete featurizer documentation.
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 splittingMaxMinSplitter: Maximize diversity between setsRandomSplitter: Random splittingRandomStratifiedSplitter: Preserves class distributions| 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 |
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)
# 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)
# 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)
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:
load_tox21(), load_bbbp(), load_hiv(), load_clintox()load_delaney(), load_freesolv(), load_lipo()load_qm7(), load_qm8(), load_qm9()load_perovskite(), load_bandgap(), load_mp_formation_energy()See references/api_reference.md for complete dataset list.
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:
Use the scripts/transfer_learning.py script for guided transfer learning workflows.
# 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)
# 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)
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}")
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])
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.
This skill includes three production-ready scripts in the scripts/ directory:
predict_solubility.pyTrain 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"
graph_neural_network.pyTrain 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
transfer_learning.pyFine-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
# 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)
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)
# 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)
# 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
Problem: Using random splitting allows similar molecules in train/test sets.
Solution: Always use ScaffoldSplitter for molecular datasets.
Problem: Graph neural networks perform worse than simple fingerprints. Solutions:
Problem: Model memorizes training data. Solutions:
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]
This skill includes comprehensive reference documentation:
references/api_reference.mdComplete API documentation including:
When to reference: Search this file when you need specific API details, parameter names, or want to explore available options.
references/workflows.mdEight detailed end-to-end workflows:
When to reference: Use these workflows as templates for implementing complete solutions.
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.
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