Deepchem Circular Featurization

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ma-compbio-lab
• Source: ma-compbio-lab/SkillFoundry
• License: Apache-2.0
• Homepage: https://ma-compbio-lab.github.io/SkillFoundry/

Install and usage instructions live in the source repository linked above.