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
npx -y skills add ma-compbio-lab/SkillFoundry --skill deepchem-circular-featurizationAssembled 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
--smilesarguments, 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, andon_bit_countfor each molecule
Requirements
slurm/envs/deepchem- DeepChem 2.8.0 and RDKit installed in that prefix
Procedure
- 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. - Inspect
size,radius, and each molecule'scanonical_smiles,bit_vector, andon_bits. - 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
What ships with it: 8 files
11.2 KB alongside SKILL.md, 2 of them executable
assets/
- aspirin_caffeine_fingerprints.json1.7 KB
- README.md155 B
examples/
- README.md702 B
scripts/
- compute_circular_fingerprints.pyruns4.0 KB
tests/
- README.md174 B
- test_deepchem_circular_featurization.pyruns2.7 KB
- metadata.yaml1.3 KB
- refs.md526 B