deepchem-circular-featurization
SkillAI & modelsUse 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.
No other account needed.
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
--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
Signals
- GitHub stars
- 39
- Forks
- 5
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
deepchem-circular-featurization- Source
- github.com/ma-compbio-lab/skillfoundry