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Deepchem circular featurization

Skill ma-compbio-lab/SkillFoundry/skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization

Use this skill to compute compact DeepChem circular fingerprints from a small set of SMILES strings with the repo-managed DeepChem prefix. Do not use it for model training, docking, or large library screening.From its SKILL.md

Install
npx -y skills add ma-compbio-lab/SkillFoundry --skill deepchem-circular-featurization

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

2.4 KB, 517 tokens by cl100k_base, as published. Nobody here has run it

Purpose

Turn one or more SMILES strings into deterministic DeepChem CircularFingerprint summaries without requiring TensorFlow or PyTorch.

When to use

  • You need a lightweight DeepChem-backed fingerprinting step before downstream molecular ML work.
  • You want a compact JSON payload with canonical SMILES, dense bit vectors, and active bit indices.

When not to use

  • You need graph featurizers, model training, or dataset download workflows.
  • You need batch-scale featurization for very large libraries.

Inputs

  • Repeated --smiles arguments, or no arguments to use the bundled aspirin/caffeine example
  • Optional --size, --radius, and --out

Outputs

  • JSON summary with canonical_smiles, bit_vector, on_bits, and on_bit_count for each molecule

Requirements

  • slurm/envs/deepchem
  • DeepChem 2.8.0 and RDKit installed in that prefix

Procedure

  1. Run slurm/envs/deepchem/bin/python skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/scripts/compute_circular_fingerprints.py --out skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/assets/aspirin_caffeine_fingerprints.json.
  2. Inspect size, radius, and each molecule's canonical_smiles, bit_vector, and on_bits.
  3. Reuse the compact JSON as a deterministic preprocessing artifact for later experiments.

Validation

  • The command exits successfully under slurm/envs/deepchem/bin/python.
  • Each molecule gets a non-empty canonical SMILES and a bit vector of the requested length.
  • Repeated runs with the same inputs produce the same fingerprint payload.

Failure modes and fixes

  • Missing DeepChem runtime: run the script with slurm/envs/deepchem/bin/python.
  • Invalid SMILES: correct the input string before featurization.
  • Optional backend warnings: TensorFlow and PyTorch are not required for this fingerprint-only skill.

Safety and limits

  • Local featurization only.
  • No activity prediction, medicinal-chemistry recommendation, or safety interpretation is implied.

Provenance

Related skills

  • rdkit-molecular-descriptors

What ships with it: 8 files

11.2 KB alongside SKILL.md, 2 of them executable

examples/

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