| name | molfeat |
| description | Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML. |
| license | Apache-2.0 license |
| allowed-tools | Read Write Edit Bash |
| compatibility | Requires Python 3.9–3.10 (molfeat 0.11.0 does not support 3.11+). Requires datamol, PyTorch, and optional extras for GNN/transformer models. |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
Molfeat - Molecular Featurization Hub
Overview
Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.
Version note: Examples target molfeat 0.11.0 (PyPI stable, May 2025). Requires Python 3.9–3.10 (requires-python caps below 3.11). Depends on datamol ≥0.8.0 and PyTorch ≥1.13. Since 0.8.7, prefer datamol Mol objects over raw rdkit.Chem.Mol. Since 0.10.1, fingerprint calculators use RDKit's rdFingerprintGenerator API internally. Since 0.11.0, pretrained models load in memory and base models are set to PyTorch evaluation mode automatically.
When to Use This Skill
This skill should be used when working with:
- Molecular machine learning: Building QSAR/QSPR models, property prediction
- Virtual screening: Ranking compound libraries for biological activity
- Similarity searching: Finding structurally similar molecules
- Chemical space analysis: Clustering, visualization, dimensionality reduction
- Deep learning: Training neural networks on molecular data
- Featurization pipelines: Converting SMILES to ML-ready representations
- Cheminformatics: Any task requiring molecular feature extraction
Installation
Use a Python 3.9 or 3.10 environment (molfeat does not install on 3.11+ as of 0.11.0):
uv pip install "molfeat==0.11.0"
uv pip install "molfeat[all]==0.11.0"
Optional dependency extras (PyPI):
molfeat[dgl] — GNN models (GIN variants); upstream recommends dgl<=2.0 (graphbolt issues in newer DGL)molfeat[graphormer] — Graphormer modelsmolfeat[transformer] — ChemBERTa, ChemGPT, MolT5molfeat[fcd] — FCD descriptorsmolfeat[pyg] — PyTorch Geometric featurizersmolfeat[viz] — NGLView visualization widgets
External featurizers: MAP4 is not bundled in molfeat extras — install from reymond-group/map4 separately. Some heavy deps (DGL, dgllife, graphormer-pretrained) are easier via conda-forge; see optional dependencies.
Core Concepts
Molfeat organizes featurization into three hierarchical classes:
1. Calculators (molfeat.calc)
Callable objects that convert individual molecules into feature vectors. Accept RDKit Chem.Mol objects or SMILES strings.
Use calculators for:
- Single molecule featurization
- Custom processing loops
- Direct feature computation
Example:
from molfeat.calc import FPCalculator
calc = FPCalculator("ecfp", radius=3, fpSize=2048)
features = calc("CCO")
2. Transformers (molfeat.trans)
Scikit-learn compatible transformers that wrap calculators for batch processing with parallelization.
Use transformers for:
- Batch featurization of molecular datasets
- Integration with scikit-learn pipelines
- Parallel processing (automatic CPU utilization)
Example:
from molfeat.trans import MoleculeTransformer
from molfeat.calc import FPCalculator
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
features = transformer(smiles_list)
3. Pretrained Transformers (molfeat.trans.pretrained)
Specialized transformers for deep learning models with batched inference and caching.
Use pretrained transformers for:
- State-of-the-art molecular embeddings
- Transfer learning from large chemical datasets
- Deep learning feature extraction
Example:
from molfeat.trans.pretrained import PretrainedMolTransformer
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)
embeddings = transformer(smiles_list)
Quick Start Workflow
Basic Featurization
import datamol as dm
from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer
smiles = ["CCO", "CC(=O)O", "c1ccccc1", "CC(C)O"]
calc = FPCalculator("ecfp", radius=3)
transformer = MoleculeTransformer(calc, n_jobs=-1)
features = transformer(smiles)
print(f"Shape: {features.shape}")
Save and Load Configuration
transformer.to_state_yaml_file("featurizer_config.yml")
loaded = MoleculeTransformer.from_state_yaml_file("featurizer_config.yml")
Handle Errors Gracefully
transformer = MoleculeTransformer(
calc,
n_jobs=-1,
ignore_errors=True,
verbose=True
)
features = transformer(smiles_with_errors)
Choosing the Right Featurizer
For Traditional Machine Learning (RF, SVM, XGBoost)
Start with fingerprints:
FPCalculator("ecfp", radius=3, fpSize=2048)
FPCalculator("maccs")
FPCalculator("map4")
For interpretable models:
from molfeat.calc import RDKitDescriptors2D
RDKitDescriptors2D()
from molfeat.calc import MordredDescriptors
MordredDescriptors()
Combine multiple featurizers:
from molfeat.trans import FeatConcat
concat = FeatConcat([
FPCalculator("maccs"),
FPCalculator("ecfp")
])
For Deep Learning
Transformer-based embeddings:
PretrainedMolTransformer("ChemBERTa-77M-MLM")
PretrainedMolTransformer("ChemGPT-1.2B")
Graph neural networks:
PretrainedMolTransformer("gin-supervised-masking")
PretrainedMolTransformer("gin-supervised-infomax")
PretrainedMolTransformer("Graphormer-pcqm4mv2")
For Similarity Searching
FPCalculator("ecfp")
FPCalculator("maccs")
FPCalculator("map4")
from molfeat.calc import USRDescriptors
USRDescriptors()
For Pharmacophore-Based Approaches
FPCalculator("fcfp")
from molfeat.calc import CATSCalculator
CATSCalculator(mode="2D")
FPCalculator("gobbi2D")
Common Workflows
Building a QSAR Model
from molfeat.trans import MoleculeTransformer
from molfeat.calc import FPCalculator
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X = transformer(smiles_train)
model = RandomForestRegressor(n_estimators=100)
scores = cross_val_score(model, X, y_train, cv=5)
print(f"R² = {scores.mean():.3f}")
transformer.to_state_yaml_file("production_featurizer.yml")
Virtual Screening Pipeline
from sklearn.ensemble import RandomForestClassifier
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X_train = transformer(train_smiles)
clf = RandomForestClassifier(n_estimators=500)
clf.fit(X_train, train_labels)
X_screen = transformer(screening_library)
predictions = clf.predict_proba(X_screen)[:, 1]
top_indices = predictions.argsort()[::-1][:1000]
top_hits = [screening_library[i] for i in top_indices]
Similarity Search
from sklearn.metrics.pairwise import cosine_similarity
calc = FPCalculator("ecfp")
query_fp = calc(query_smiles).reshape(1, -1)
transformer = MoleculeTransformer(calc, n_jobs=-1)
database_fps = transformer(database_smiles)
similarities = cosine_similarity(query_fp, database_fps)[0]
top_similar = similarities.argsort()[-10:][::-1]
Scikit-learn Pipeline Integration
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
pipeline = Pipeline([
('featurizer', MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)),
('classifier', RandomForestClassifier(n_estimators=100))
])
pipeline.fit(smiles_train, y_train)
predictions = pipeline.predict(smiles_test)
Comparing Multiple Featurizers
featurizers = {
'ECFP': FPCalculator("ecfp"),
'MACCS': FPCalculator("maccs"),
'Descriptors': RDKitDescriptors2D(),
'ChemBERTa': PretrainedMolTransformer("ChemBERTa-77M-MLM")
}
results = {}
for name, feat in featurizers.items():
transformer = MoleculeTransformer(feat, n_jobs=-1)
X = transformer(smiles)
score = score_model(X, y)
results[name] = score
Discovering Available Featurizers
Use the ModelStore to explore all available featurizers:
from molfeat.store.modelstore import ModelStore
store = ModelStore()
all_models = store.available_models
print(f"Total featurizers: {len(all_models)}")
chemberta_models = store.search(name="ChemBERTa")
for model in chemberta_models:
print(f"- {model.name}: {model.description}")
model_card = store.search(name="ChemBERTa-77M-MLM")[0]
model_card.usage()
transformer = store.load("ChemBERTa-77M-MLM")
Advanced Features
Custom Preprocessing
class CustomTransformer(MoleculeTransformer):
def preprocess(self, mol):
"""Custom preprocessing pipeline"""
if isinstance(mol, str):
mol = dm.to_mol(mol)
mol = dm.standardize_mol(mol)
mol = dm.remove_salts(mol)
return mol
transformer = CustomTransformer(FPCalculator("ecfp"), n_jobs=-1)
Batch Processing Large Datasets
import numpy as np
def featurize_in_chunks(smiles_list, transformer, chunk_size=10000):
"""Process large datasets in chunks to manage memory"""
all_features = []
for i in range(0, len(smiles_list), chunk_size):
chunk = smiles_list[i:i+chunk_size]
features = transformer(chunk)
all_features.append(features)
return np.vstack(all_features)
Caching Expensive Embeddings
Prefer molfeat's built-in pretrained-model cache when possible. For custom embedding caches, use NumPy arrays instead of pickle (pickle can execute arbitrary code when loading untrusted files):
import numpy as np
from pathlib import Path
cache_file = Path("embeddings_cache.npz")
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)
if cache_file.exists():
embeddings = np.load(cache_file)["embeddings"]
else:
embeddings = transformer(smiles_list)
np.savez(cache_file, embeddings=embeddings)
Performance Tips
- Use parallelization: Set
n_jobs=-1 to utilize all CPU cores
- Batch processing: Process multiple molecules at once instead of loops
- Choose appropriate featurizers: Fingerprints are faster than deep learning models
- Cache pretrained models: Leverage built-in caching for repeated use
- Use float32: Set
dtype=np.float32 when precision allows
- Handle errors efficiently: Use
ignore_errors=True for large datasets
Common Featurizers Reference
Quick reference for frequently used featurizers:
| Featurizer | Type | Dimensions | Speed | Use Case |
|---|
ecfp | Fingerprint | 2048 | Fast | General purpose |
maccs | Fingerprint | 167 | Very fast | Scaffold similarity |
desc2D | Descriptors | 200+ | Fast | Interpretable models |
mordred | Descriptors | 1800+ | Medium | Comprehensive features |
map4 | Fingerprint | 1024 | Fast | Large-scale screening |
ChemBERTa-77M-MLM | Deep learning | 768 | Slow* | Transfer learning |
gin-supervised-masking | GNN | Variable | Slow* | Graph-based models |
*First run is slow; subsequent runs benefit from caching
Resources
This skill includes comprehensive reference documentation:
references/api_reference.md
Complete API documentation covering:
molfeat.calc - All calculator classes and parametersmolfeat.trans - Transformer classes and methodsmolfeat.store - ModelStore usage- Common patterns and integration examples
- Performance optimization tips
When to load: Reference when implementing specific calculators, understanding transformer parameters, or integrating with scikit-learn/PyTorch.
references/available_featurizers.md
Comprehensive catalog of all 100+ featurizers organized by category:
- Transformer-based language models (ChemBERTa, ChemGPT)
- Graph neural networks (GIN, Graphormer)
- Molecular descriptors (RDKit, Mordred)
- Fingerprints (ECFP, MACCS, MAP4, and 15+ others)
- Pharmacophore descriptors (CATS, Gobbi)
- Shape descriptors (USR, ElectroShape)
- Scaffold-based descriptors
When to load: Reference when selecting the optimal featurizer for a specific task, exploring available options, or understanding featurizer characteristics.
Search tip: Use grep to find specific featurizer types:
grep -i "chembert" references/available_featurizers.md
grep -i "pharmacophore" references/available_featurizers.md
references/examples.md
Practical code examples for common scenarios:
- Installation and quick start
- Calculator and transformer examples
- Pretrained model usage
- Scikit-learn and PyTorch integration
- Virtual screening workflows
- QSAR model building
- Similarity searching
- Troubleshooting and best practices
When to load: Reference when implementing specific workflows, troubleshooting issues, or learning molfeat patterns.
Troubleshooting
Invalid Molecules
Enable error handling to skip invalid SMILES:
transformer = MoleculeTransformer(
calc,
ignore_errors=True,
verbose=True
)
Memory Issues with Large Datasets
Process in chunks or use streaming approaches for datasets > 100K molecules.
Pretrained Model Dependencies
Some models require additional packages. Install specific extras (pin version for reproducibility):
uv pip install "molfeat[transformer]==0.11.0"
uv pip install "molfeat[dgl]==0.11.0"
uv pip install "molfeat[graphormer]==0.11.0"
Reproducibility
Save exact configurations and document versions:
transformer.to_state_yaml_file("config.yml")
import molfeat
print(f"molfeat version: {molfeat.__version__}")
Additional Resources
