molfeat

# 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):

```bash
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](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.

**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 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/choosing_a_featurizer.md](references/choosing_a_featurizer.md).

The full featurizer list is in
[references/available_featurizers.md](references/available_featurizers.md); more examples
are in [references/examples.md](references/examples.md).

## 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.a