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

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) # Deep learning embeddings

Quick Start Workflow

Basic Featurization

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

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

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:

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:

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:

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:

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 similarity

from molfeat.calc import USRDescriptors USRDescriptors()

For Pharmacophore-Based Approaches

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

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]

Similarity Search

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

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")

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

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

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

• 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:

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:

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

Reproducibility

Save exact configurations and document versions:

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