Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
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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
metadata
{"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.
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
uv pip install molfeat
# With all optional dependencies
uv pip install "molfeat[all]"
Optional dependencies for specific featurizers:
molfeat[dgl] - GNN models (GIN variants)
molfeat[graphormer] - Graphormer models
molfeat[transformer] - ChemBERTa, ChemGPT, MolT5
molfeat[fcd] - FCD descriptors
molfeat[map4] - MAP4 fingerprints
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") # Returns numpy array (2048,)
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) # Parallel processing
# ChemBERTa - Pre-trained on 77M PubChem compounds
PretrainedMolTransformer("ChemBERTa-77M-MLM")
# ChemGPT - Autoregressive language model
PretrainedMolTransformer("ChemGPT-1.2B")
Graph neural networks:
# GIN models with different pre-training objectives
PretrainedMolTransformer("gin-supervised-masking")
PretrainedMolTransformer("gin-supervised-infomax")
# Graphormer for quantum chemistry
PretrainedMolTransformer("Graphormer-pcqm4mv2")
For Similarity Searching
# ECFP - General purpose, most widely used
FPCalculator("ecfp")
# MACCS - Fast, scaffold-based similarity
FPCalculator("maccs")
# MAP4 - Efficient for large databases
FPCalculator("map4")
# USR/USRCAT - 3D shape similarityfrom molfeat.calc import USRDescriptors
USRDescriptors()
For Pharmacophore-Based Approaches
# FCFP - Functional group based
FPCalculator("fcfp")
# CATS - Pharmacophore pair distributionsfrom molfeat.calc import CATSCalculator
CATSCalculator(mode="2D")
# Gobbi - Explicit pharmacophore features
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
# Featurize molecules
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X = transformer(smiles_train)
# Train model
model = RandomForestRegressor(n_estimators=100)
scores = cross_val_score(model, X, y_train, cv=5)
print(f"R² = {scores.mean():.3f}")
# Save configuration for deployment
transformer.to_state_yaml_file("production_featurizer.yml")
Virtual Screening Pipeline
from sklearn.ensemble import RandomForestClassifier
# Train on known actives/inactives
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X_train = transformer(train_smiles)
clf = RandomForestClassifier(n_estimators=500)
clf.fit(X_train, train_labels)
# Screen large library
X_screen = transformer(screening_library) # e.g., 1M compounds
predictions = clf.predict_proba(X_screen)[:, 1]
# Rank and select top hits
top_indices = predictions.argsort()[::-1][:1000]
top_hits = [screening_library[i] for i in top_indices]
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
# Create end-to-end pipeline
pipeline = Pipeline([
('featurizer', MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)),
('classifier', RandomForestClassifier(n_estimators=100))
])
# Train and predict directly on SMILES
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)
# Evaluate with your ML model
score = evaluate_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()
# List all available models
all_models = store.available_models
print(f"Total featurizers: {len(all_models)}")
# Search for specific models
chemberta_models = store.search(name="ChemBERTa")
for model in chemberta_models:
print(f"- {model.name}: {model.description}")
# Get usage information
model_card = store.search(name="ChemBERTa-77M-MLM")[0]
model_card.usage() # Display usage examples# Load model
transformer = store.load("ChemBERTa-77M-MLM")
deffeaturize_in_chunks(smiles_list, transformer, chunk_size=10000):
"""Process large datasets in chunks to manage memory"""
all_features = []
for i inrange(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)
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 parameters
molfeat.trans - Transformer classes and methods
molfeat.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:
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