deepchem-circular-featurization

name	deepchem-circular-featurization
description	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.

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

• DeepChem documentation: https://deepchem.readthedocs.io/en/latest/
• RDKit documentation: https://www.rdkit.org/docs/index.html

Related skills

• rdkit-molecular-descriptors