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.
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Stars31,545
Forks3,146
LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
</> SKILL.md
molfeat.SKILL.md
---name: molfeat
description: 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
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.9–3.10 (molfeat 0.11.0 does not support 3.11+). Requires datamol, PyTorch, and optional extras for GNN/transformer models.
metadata: {"version": "1.0", "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.
**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:
**External featurizers:** MAP4 is not bundled in molfeat extras — install from [reymond-group/map4](https://github.com/reymond-group/map4) separately. Some heavy deps (DGL, dgllife, graphormer-pretrained) are easier via conda-forge; see [optional dependencies](https://molfeat-docs.datamol.io/stable/).
## 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.
"""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):
```python
import numpy as np
from pathlib import Path
cache_file = Path("embeddings_cache.npz") # fixed path under your project
*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:
