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Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
Passed 21 of 23 test cases with the skill, and 17 of 23 without it.
and 2 more in the full report
4 fixed17 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
For deep learning data augmentation on molecular SMILES in Python, developers sometimes try manually shuffling atom order. What dedicated function generates a vector of randomized non-canonical SMILES strings with a fixed random seed?
| Checked | Without skill | With skill |
|---|---|---|
| MolToRandomSmilesVect is called with numSmiles and randomSeed parameters. | Missed | Passed |
The instructions your model reads when the skill loads.
RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks.
Reading Molecules:
Read molecular structures from various formats:
from rdkit import Chem
# From SMILES strings
mol = Chem.MolFromSmiles('Cc1ccccc1') # Returns Mol object or None
# From MOL files
mol = Chem.MolFromMolFile('path/to/file.mol')
# From MOL blocks (string data)
mol = Chem.MolFromMolBlock(mol_block_string)
# From InChI
mol = Chem.MolFromInchi('InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H')
Writing Molecules:
Convert molecules to text representations:
# To canonical SMILES
smiles = Chem.MolToSmiles(mol)
# To MOL block
mol_block = Chem.MolToMolBlock(mol)
test cases passed, vs 17 / 23 without
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