molfeat
Category: chemistry-and-drug-discovery · License: Apache-2.0 license · Version: 1.2
Tags: molecular featurization, fingerprints, descriptors, cheminformatics, machine learning
When to use it
Converts SMILES strings or RDKit/datamol molecules into numerical features using molfeat (0.11.0), which provides calculators, scikit-learn compatible transformers, and pretrained embedding models. Covers fingerprints (ECFP, MACCS, MAP4), RDKit and Mordred descriptors, pharmacophore descriptors, and pretrained models such as ChemBERTa and GIN, with parallel processing and caching. Use when building QSAR or QSPR models from SMILES. Use when choosing among molecular featurizers for a property prediction task. Use when generating embeddings for virtual screening, similarity search, or chemical space clustering. Use when adding a featurizer to a scikit-learn pipeline. Not for general cheminformatics tasks such as reading or editing structures, which belong to RDKit itself.Example requests
Ask your agent in plain language, for example:- “what’s the best molfeat featurizer for a small solubility dataset?”
- “I have a file kinase_hits.csv with a ‘smiles’ column, about 40k rows. I’m trying to run
from molfeat.trans.pretrained import PretrainedHFTransformerwith ChemBERTa-77M-MLM to get embeddings for clustering, but pip install molfeat fails on my Python 3.11 env. How do I fix it and get the embeddings?” - “I want to turn a list of drug-like SMILES into numeric vectors that I can drop into a scikit-learn Pipeline with a random forest to predict IC50, and I need it to run on all my CPU cores and not crash on a few bad SMILES. How should I set that up?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole chemistry-and-drug-discovery category with /plugin install chemistry-and-drug-discovery@research-agent-skills.