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

bash
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:

python
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:

python
from molfeat.trans import MoleculeTransformer
from molfeat.calc import FPCalculator

transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
features = transformer(smiles_list)  # Parallel processing

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:

python
from molfeat.trans.pretrained import PretrainedMolTransformer

transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)
embeddings = transformer(smiles_list)  # Deep learning embeddings

Quick Start Workflow

Basic Featurization

python
import datamol as dm
from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer

# Load molecular data
smiles = ["CCO", "CC(=O)O", "c1ccccc1", "CC(C)O"]

# Create calculator and transformer
calc = FPCalculator("ecfp", radius=3)
transformer = MoleculeTransformer(calc, n_jobs=-1)

# Featurize molecules
features = transformer(smiles)
print(f"Shape: {features.shape}")  # (4, 2048)

Save and Load Configuration

python
# Save featurizer configuration for reproducibility
transformer.to_state_yaml_file("featurizer_config.yml")

# Reload exact configuration
loaded = MoleculeTransformer.from_state_yaml_file("featurizer_config.yml")

Handle Errors Gracefully

python
# Process dataset with potentially invalid SMILES
transformer = MoleculeTransformer(
    calc,
    n_jobs=-1,
    ignore_errors=True,  # Continue on failures
    verbose=True          # Log error details
)

features = transformer(smiles_with_errors)
# Returns None for failed molecules

Choosing the Right Featurizer

For Traditional Machine Learning (RF, SVM, XGBoost)

Start with fingerprints:

python
# ECFP - Most popular, general-purpose
FPCalculator("ecfp", radius=3, fpSize=2048)

# MACCS - Fast, good for scaffold hopping
FPCalculator("maccs")

# MAP4 - Efficient for large-scale screening
FPCalculator("map4")

For interpretable models:

python
# RDKit 2D descriptors (200+ named properties)
from molfeat.calc import RDKitDescriptors2D
RDKitDescriptors2D()

# Mordred (1800+ comprehensive descriptors)
from molfeat.calc import MordredDescriptors
MordredDescriptors()

Combine multiple featurizers:

python
from molfeat.trans import FeatConcat

concat = FeatConcat([
    FPCalculator("maccs"),      # 167 dimensions
    FPCalculator("ecfp")         # 2048 dimensions
])  # Result: 2215-dimensional combined features

For Deep Learning

Transformer-based embeddings:

python
# ChemBERTa - Pre-trained on 77M PubChem compounds
PretrainedMolTransformer("ChemBERTa-77M-MLM")

# ChemGPT - Autoregressive language model
PretrainedMolTransformer("ChemGPT-1.2B")

Graph neural networks:

python
# 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

python
# 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 similarity
from molfeat.calc import USRDescriptors
USRDescriptors()

For Pharmacophore-Based Approaches

python
# FCFP - Functional group based
FPCalculator("fcfp")

# CATS - Pharmacophore pair distributions
from molfeat.calc import CATSCalculator
CATSCalculator(mode="2D")

# Gobbi - Explicit pharmacophore features
FPCalculator("gobbi2D")

Common Workflows

Building a QSAR Model

python
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

python
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]

Similarity Search

python
from sklearn.metrics.pairwise import cosine_similarity

# Query molecule
calc = FPCalculator("ecfp")
query_fp = calc(query_smiles).reshape(1, -1)

# Database fingerprints
transformer = MoleculeTransformer(calc, n_jobs=-1)
database_fps = transformer(database_smiles)

# Compute similarity
similarities = cosine_similarity(query_fp, database_fps)[0]
top_similar = similarities.argsort()[-10:][::-1]

Scikit-learn Pipeline Integration

python
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

python
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:

python
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")

Advanced Features

Custom Preprocessing

python
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

python
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

python
import pickle

cache_file = "embeddings_cache.pkl"
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)

try:
    with open(cache_file, "rb") as f:
        embeddings = pickle.load(f)
except FileNotFoundError:
    embeddings = transformer(smiles_list)
    with open(cache_file, "wb") as f:
        pickle.dump(embeddings, f)

Performance Tips

1. Use parallelization: Set n_jobs=-1 to utilize all CPU cores
2. Batch processing: Process multiple molecules at once instead of loops
3. Choose appropriate featurizers: Fingerprints are faster than deep learning models
4. Cache pretrained models: Leverage built-in caching for repeated use
5. Use float32: Set dtype=np.float32 when precision allows
6. 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:

bash
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:

python
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:

bash
uv pip install "molfeat[transformer]"  # For ChemBERTa/ChemGPT
uv pip install "molfeat[dgl]"          # For GIN models

Reproducibility

Save exact configurations and document versions:

python
transformer.to_state_yaml_file("config.yml")
import molfeat
print(f"molfeat version: {molfeat.__version__}")

Additional Resources

• Official Documentation: https://molfeat-docs.datamol.io/
• GitHub Repository: https://github.com/datamol-io/molfeat
• PyPI Package: https://pypi.org/project/molfeat/
• Tutorial: https://portal.valencelabs.com/datamol/post/types-of-featurizers-b1e8HHrbFMkbun6