deepchem-circular-featurization

SkillAI & models

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the deepchem-circular-featurization skill

What this skill tells your AI

The instructions your AI receives, as published by ma-compbio-lab/skillfoundry in skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
39
Forks
5
Last commit
Sep 2026
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Catalog kind
skill
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deepchem-circular-featurization
Source
github.com/ma-compbio-lab/skillfoundry