Skip to content
Get packs

Molfeat Molecule Featurizer

Turns small molecules into fingerprints, descriptors and embeddings for molecular ML

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
5 files

What it does

This skill teaches an AI agent how to convert chemical structures (written as SMILES strings or RDKit molecules) into numerical features that machine learning models can use. When installed, the agent can build fingerprints like ECFP and MACCS, compute RDKit descriptors, pharmacophore and 3D shape features, or pretrained language-model embeddings, while following careful practices such as keeping record IDs aligned, fitting preprocessing inside training folds, and saving featurizer configuration so results are reproducible.

Great for

  • Chemists or data scientists building QSAR/QSPR or similarity-search models
  • Developers writing molecular machine learning pipelines with Molfeat 1.x
  • Researchers who need reproducible molecular featurization with saved configurations

Skip it if

  • Non-chemistry data or general text/tabular featurization, where it has no use
  • Clinical or safety-critical decisions based on model outputs, since the skill only supports feature engineering, not validated predictions
  • Loading untrusted featurizer state files, because saved state can reference Python classes and custom callables

Try asking

  • “Compute ECFP4 fingerprints for a list of SMILES strings and handle invalid molecules without misaligning labels”
  • “Build a reproducible featurizer config and save it to YAML so I can reload it later”
  • “Compare ECFP, MACCS and RDKit descriptors for a scaffold-split bioactivity dataset”
  • “Generate ChemBERTa embeddings for a set of molecules with mean pooling”
Step-by-step guide: how to use Molfeat Molecule FeaturizerInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 5 original files, the MIT licence and an attribution note.

    molfeat.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip molfeat.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip molfeat.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Create an isolated virtual environment and install molfeat==1.0.0 before running the examples, since the skill pins specific behaviour for that version.
  • Check licences and sizes of pretrained model weights before downloading them, and keep the model revision with any saved embeddings.
  • Only load featurizer YAML files from trusted sources, and ask the agent to revalidate old saved state after migrating to 1.x.

What's inside

  • references/api_reference.md
  • references/available_featurizers.md
  • references/choosing_a_featurizer.md
  • references/examples.md
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.