Deepchem Molgraph Featurization
Use this skill to featurize SMILES strings into DeepChem molecular graph objects. Prefer it for local graph-based preprocessing before molecular machine-learning experiments.
Install
$ agentstack add skill-ma-compbio-lab-skillfoundry-deepchem-molgraph-featurization ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ma-compbio-lab-skillfoundry-deepchem-molgraph-featurization) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Purpose
Create deterministic DeepChem molecular graph features from a small SMILES table using the repo-managed chemtools prefix.
When to use
- You need a lightweight DeepChem starter without training a model.
- You want to inspect graph sizes and node-feature dimensions before modeling.
When not to use
- You need trained DeepChem models requiring TensorFlow or PyTorch.
- You need dataset download or benchmark automation.
Inputs
- A TSV file with
molecule_idandsmilescolumns.
Outputs
- A JSON summary with graph dimensions for each molecule.
Requirements
slurm/envs/chemtools- DeepChem and RDKit in that prefix
Procedure
- Run
slurm/envs/chemtools/bin/python skills/computational-chemistry-and-molecular-simulation/deepchem-molgraph-featurization/scripts/featurize_molecules.py --input skills/computational-chemistry-and-molecular-simulation/deepchem-molgraph-featurization/examples/molecules.tsv --out scratch/deepchem/featurization.json. - Inspect node counts, edge counts, and feature dimensions.
- Use the feature summary as a preflight step before larger molecular ML runs.
Validation
- The script exits successfully.
- Each molecule yields graph metadata.
- Node and edge counts are positive for valid molecules.
Failure modes and fixes
- Missing optional ML backends: this skill only requires the featurizer path, not
torchortensorflow. - Invalid SMILES: correct the input row before featurization.
Provenance
- DeepChem documentation: https://deepchem.readthedocs.io/en/latest/
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.
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Versions
- v0.1.0 Imported from the upstream source.