/molfeat

name: molfeat
description: Featurizes small molecules with Molfeat for QSAR/QSPR, chemical similarity, virtual screening, and molecular ML. Covers ECFP/MACCS fingerprints, RDKit descriptors, pharmacophores, pretrained embeddings, configuration persistence, and molecule-to-label alignment.
license: Apache-2.0 license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.11+ and molfeat 1.0.0 (RDKit, datamol, PyTorch). macOS Intel needs Python 3.11–3.12 and upstream platform-specific dependency pins. Optional extras and network access are needed for pretrained models; core fingerprints run offline.
metadata:
  version: "2.0"
  skill-author: K-Dense Inc.
  last-reviewed: "2026-10-01"
  upstream-version: "1.0.0"

Molfeat — Small-molecule featurization

When to use

Use this skill to turn SMILES or RDKit molecules into fingerprints, descriptors, pharmacophores, or pretrained embeddings for molecular machine learning and similarity search. Compare representations on the same molecular split and assay endpoint.

This skill targets **Molfeat 1.0.0**. The previous 0.11 runtime guidance is obsolete: 1.x supports modern Python and removes DGL/DGLLife, legacy Graphormer, and protein adapters. Historical model-store cards can still name removed adapters. The tagged [1.0 migration guide](https://github.com/datamol-io/molfeat/blob/1.0.0/docs/migration.md) and source take precedence over older pages still served at the documentation's `stable` URL.

Installation

Create an isolated environment. Core examples were executed on Python 3.13.3/macOS Apple Silicon with Molfeat 1.0.0, datamol 0.13.0, RDKit 2026.03.6, and PyTorch 2.14.1.

uv venv --python 3.13 .venv-molfeat
uv pip install --python .venv-molfeat/bin/python "molfeat==1.0.0"

On Windows use `.venv-molfeat\Scripts\python.exe` as the interpreter path. Upstream supports Python 3.11–3.14; macOS Intel uses Python 3.11–3.12, PyTorch 2.2.x, NumPy<2, and Transformers<5. Other platforms require PyTorch>=2.5. Let Molfeat's platform markers resolve these constraints; do not copy Apple Silicon pins to Intel.

Install only needed extras with the same interpreter: `molfeat[transformer]==1.0.0` for Hugging Face models, `[mordred]` for mordredcommunity, `[pyg]` for graph tensors, `[fcd]` for ChemNet embeddings, `[selfies]` for SELFIES conversion, `[cache]` for HDF5/Parquet, `[viz]` for visualization, and `[cloud]` for S3/GCS stores. DGL and Graphormer extras no longer exist. Pretrained inference may download substantial weights; check model licensing, disk space, and device requirements first.

Workflow

1. Preserve source record IDs, labels, original structures, and a declared policy for salts, stereochemistry, tautomers, charge, and duplicate compounds. Standardization changes chemical identity; apply the same policy to training and prediction. 2. Start with an explicit ECFP baseline. A calculator processes one molecule; `MoleculeTransformer` batches it. `datamol.Mol` is RDKit's molecule type. 3. Record rejected positions and align every associated array. Inspect descriptor NaN/Inf separately: successful parsing does not guarantee finite features. 4. Fit any feature selection, imputation, scaling, and model inside the training fold. Prefer scaffold/group or temporal splits appropriate to the scientific question; random splits can leak close analogues or repeated measurements. 5. Save the featurizer state, ordered feature names, package versions, molecular preprocessing policy, and input IDs. Revalidate old state after migrating to 1.x.

Fingerprint baseline and invalid records

import numpy as np
from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer

smiles = ["CCO", "invalid", "CC(=O)O", "c1ccccc1"]
record_ids = np.array(["ethanol", "rejected", "acetate", "benzene"])
y = np.array([0.1, 9.9, 0.2, 0.3])  # toy labels only
calc = FPCalculator("ecfp", radius=2, fpSize=2048, includeChirality=True)
transformer = MoleculeTransformer(calc, n_jobs=1, dtype=np.float32)
X, valid_ids = transformer(smiles, ignore_errors=True)
assert X.shape == (3, 2048)
X_ids, y_valid = record_ids[valid_ids], y[valid_ids]
assert valid_ids == [0, 2, 3]
assert np.isfinite(X).all()

`ignore_errors` belongs on the call, not the constructor. With `True`, `__call__` returns filtered features and original input positions; with `False`, it raises on failed molecules. `transform(..., ignore_errors=True)` preserves positions using `None` for failures. Never filter each feature block independently and then concatenate.

ECFP radius is a bond radius: `radius=2` means ECFP4; radius 3 means ECFP6. In 1.0.0 `FPCalculator("ecfp")` defaults to radius 2 and 2048 bits. `FPVecTransformer` has a *different* default length of 2000, so specify `length=2048` when using it.

Configuration round trip

transformer.to_state_yaml_file("featurizer.yml")
loaded = MoleculeTransformer.from_state_yaml_file("featurizer.yml")
np.testing.assert_array_equal(loaded(["CCO"]), transformer(["CCO"]))

Load only trusted configuration/artifacts. State can identify Python classes and custom serialized callables; YAML/JSON does not make arbitrary third-party state safe. State saves configuration, not assay labels, preprocessing decisions, or a trained QSAR model.

Pretrained embeddings

Illustrative; imports and signatures were checked, but no model weights were downloaded:

from molfeat.trans.pretrained import PretrainedHFTransformer

embedder = PretrainedHFTransformer(
    kind="ChemBERTa-77M-MLM", pooling="mean", concat_layers=-1,
    device="cpu", max_length=128, preload=False,
)
# First inference downloads/loads the model.
# embeddings = embedder(["CCO", "c1ccccc1"])

`PretrainedMolTransformer` is a base class, not a model-name factory. Use the concrete adapter. Embedding width depends on checkpoint, pooling, and selected layers; do not assume 768. Inspect token lengths: truncation