molfeat skill

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"

# With all pip-installable optional dependencies
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 models
• molfeat[transformer] — ChemBERTa, ChemGPT, MolT5
• molfeat[fcd] — FCD descriptors
• molfeat[pyg] — PyTorch Geometric featurizers
• molfeat[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")  # 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 a Featurizer and Common Workflows

Featurizer choice by task — traditional ML (RF, SVM, XGBoost), deep learning, similarity searching, and pharmacophore-based approaches — plus worked workflows for QSAR model building, virtual screening, similarity search, scikit-learn pipeline integration, and comparing multiple featurizers, are in references/choosingafeaturizer.md.

The full featurizer list is in references/available_featurizers.md; more examples are in references/examples.md.

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

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")  # fixed path under your project
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

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:

*First run is slow; subsequent runs benefit from caching

Resources