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Encode molecule SMILES columns into RDKit descriptor features for BO.
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Based on SOC occupation classification
Orchestrate an end-to-end chemistry or materials optimization study from a plain-English research question to BO execution and a paper draft.
BO execution layer — initializes a run from a resolved experiment spec, records observations, and continues through suggest/observe/report.
Initialize a BO run from a dataset or explicit search-space JSON.
Generate a final BO report and summarize optimization status.
Run an external evaluator loop for a BO run using a pre-provisioned backend id.
Design and stabilize an expensive or fragile chemistry evaluator before BO setup.
| name | bo-encode-molecule-descriptors |
| description | Encode molecule SMILES columns into RDKit descriptor features for BO. |
Use this skill when the user has a CSV with one or more molecule SMILES columns and wants to convert it into numerical descriptor features for the BO engine.
uv run python -m bo_workflow.converters.molecule_descriptors encode \
--input <CSV_PATH> --output-dir <DIR> --smiles-cols <COL1> [<COL2> ...]
Optional flags: --morgan-bits <N> (default 64).
features_csv — path to descriptor features CSV (descriptor/fingerprint columns + passthrough columns)catalog_csv — path to catalog CSV (same descriptors + original SMILES columns for decode)rows — number of rows encodeddescriptor_columns — number of descriptor columns generatedpassthrough_columns — non-SMILES columns carried through from the inputfeatures.csv is ready for init --dataset.catalog.csv is required by decode to map BO suggestions back to real molecules.