Molfeat — Molecular Featurization Hub

Molfeat — Molecular Featurization Hub

Overview

Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers under a scikit-learn compatible API. Convert SMILES strings into numerical representations (fingerprints, descriptors, deep learning embeddings) for QSAR modeling, virtual screening, similarity searching, and chemical space analysis.

When to Use

• Building QSAR/QSPR models requiring molecular features as input
• Virtual screening — ranking compound libraries by predicted activity
• Similarity searching against molecular databases
• Chemical space analysis — clustering, visualization, dimensionality reduction
• Deep learning on molecules using pretrained embeddings (ChemBERTa, GIN)
• Featurization pipelines integrating with scikit-learn or PyTorch
• Comparing multiple molecular representations for benchmarking
• For molecular manipulation and filtering use datamol instead; for substructure-based molecular operations use rdkit-cheminformatics

Prerequisites

uv pip install molfeat

# Optional extras for specific featurizer types
uv pip install "molfeat[transformer]"   # ChemBERTa, ChemGPT, MolT5
uv pip install "molfeat[dgl]"           # GIN graph neural networks
uv pip install "molfeat[graphormer]"    # Graphormer models
uv pip install "molfeat[fcd]"           # FCD descriptors
uv pip install "molfeat[map4]"          # MAP4 fingerprints
uv pip install "molfeat[all]"           # All dependencies

Quick Start

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

smiles = ["CCO", "CC(=O)O", "c1ccccc1", "CC(C)O"]

# Create fingerprint calculator + transformer