deepchem-molgraph-featurization

Files (10)

• README.md
• SKILL.md
• README.md · assets/
• README.md · examples/
• molecules.tsv · examples/
• metadata.yaml
• refs.md
• featurize_molecules.py · scripts/
• README.md · tests/
• test_featurize_molecules.py · tests/

SKILL.md (first lines)

## 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_id` and `smiles` columns.

## Outputs
- A JSON summary with graph dimensions for each molecule.

## Requirements
- `slurm/envs/chemtools`
- DeepChem and RDKit in that prefix

## Procedure
1. 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`.
2. Inspect node counts, edge counts, and feature dimensions.
3. 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 `torch` or `tensorflow`.
- Invalid SMILES: correct the input row before featurization.

## Provenance
- DeepChem documentation: https://deepchem.readthedocs.io/en/latest/

## Related skills
- `rdkit-molecular-descriptors`

From ma-compbio-lab/SkillFoundry, Apache-2.0.

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