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name: molecule-io-prep description: "Guides agents using datamol to construct, standardize, convert, validate, read, write, and tabularize molecules from SMILES, InChI, SMARTS, SELFIES, SDF, CSV, Excel, and dataframe inputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Molecule IO and Preparation

Use this sub-skill when a task asks for datamol molecule loading, conversion, cleanup, tabular IO, bundled toy datasets, molecule properties, salts/solvents, neutralization, or molar unit conversion.

Route By Task

• Build molecules from strings or RDKit Mol objects with dm.to_mol, dm.from_inchi, dm.from_selfies, and dm.from_smarts; use dm.to_mol for SMILES because no public dm.from_smiles API is exported.
• Convert molecules with dm.to_smiles, dm.to_inchi, dm.to_selfies, dm.to_molblock, dm.to_pdbblock, dm.to_df, and dm.from_df.
• Read or write molecule tables and files with dm.read_csv, dm.read_excel, dm.read_sdf, dm.to_sdf, dm.read_smi, dm.read_molblock, dm.read_mol2file, dm.open_df, and dm.save_df.
• Clean molecules with dm.sanitize_mol, dm.standardize_mol, dm.fix_mol, dm.remove_salts_solvents, dm.keep_largest_fragment, and dm.to_neutral.
• Manage molecule properties with dm.set_mol_props, dm.copy_mol_props, dm.clear_mol_props, and Mol.GetPropsAsDict().
• Load small bundled datasets with dm.data.freesolv, dm.data.cdk2, dm.data.solubility, dm.data.chembl_drugs, and dm.data.chembl_samples.
• Convert concentration units with dm.molar.molar_to_log and dm.molar.log_to_molar.

Local References

• API signatures and parameter choices: references/api-reference.md
• End-to-end recipes: references/workflows.md
• Format, dataframe, and property rules: references/data-formats.md
• Common failures and fixes: references/troubleshooting.md
• Safe executable smoke check: scripts/molecule_io_smoke.py

Boundaries

• Stay in this sub-skill for molecule construction, format conversion, file IO, molecule cleanup, properties, bundled data, and molar helper functions.
• Route fingerprints, arrays, distance matrices, clustering, and diversity picking to the fingerprints-similarity sub-skill after this sub-skill returns clean Mol objects or canonical SMILES.
• Route conformers, alignment, scaffolds, fragments, reactions, and isomers to the structure-generation sub-skill after this sub-skill handles parsing and standardization.
• Route rendering, molecule images, and highlight images to the visualization-utilities sub-skill.