fix: convert enforce_subs strings to Mol objects in fuzzy_scaffolding

[rtmalikian]

Summary

Fixes dm.scaffold.fuzzy_scaffolding() crashing when enforce_subs is passed as a list of strings (SMILES or SMARTS).

The function signature declares enforce_subs: Optional[List[str]], but line 227 passes each string element directly to rgp.GetSubstructMatch(frag), which expects an RDKit Mol object — raising ArgumentError on any string input.

Root Cause

# Before (line 227): frag is a str from enforce_subs
rgp.GetSubstructMatch(frag)  # ArgumentError: str is not a Mol

GetSubstructMatch requires an RDKit ROMol query, not a Python string. The parameter type hint says List[str], but the implementation assumed Mol objects.

Fix

Convert string substructures to RDKit Mol objects via Chem.MolFromSmarts() early in the function, before they're used in GetSubstructMatch:

enforce_subs_mols = []
for sub in enforce_subs:
    if isinstance(sub, str):
        mol_sub = Chem.MolFromSmarts(sub)
        if mol_sub is not None:
            enforce_subs_mols.append(mol_sub)
    else:
        enforce_subs_mols.append(sub)

Use enforce_subs_mols instead of enforce_subs in the GetSubstructMatch call.

Test Output

tests/test_scaffold.py::test_fuzzy_scaffolding PASSED
tests/test_scaffold.py::test_fuzzy_scaffolding_with_enforce_subs PASSED
2 passed in 32.89s

Before fix (demonstrating the bug):

ArgumentError: Python argument types in
    Mol.GetSubstructMatch(Mol, str)
did not match C++ signature:
    GetSubstructMatch(RDKit::ROMol self, RDKit::ROMol query, ...)

After fix:

Success with Mol arg: (0, 1, 2, 3, 4, 5)

Verification

• Existing test_fuzzy_scaffolding still passes (no regression)
• New test_fuzzy_scaffolding_with_enforce_subs passes with both SMILES and SMARTS strings
• Confirmed original code raises ArgumentError with string args
• Fix correctly converts strings to Mol via MolFromSmarts

Closes #119

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About the Author: Raphael Malikian — Clinical AI Solutions Architect. I specialise in building and fixing AI/ML systems for healthcare, including vector databases, RAG pipelines, and clinical NLP. If you need help with your project or think I can add value to your organisation, feel free to reach out — I'd love to connect.

📧 rtmalikian@gmail.com
🔗 GitHub: https://github.com/rtmalikian
🔗 LinkedIn: http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a

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Disclosure: This code was developed with assistance from mimo-2.5-pro (Xiaomi) via Hermes Agent (Nous Research). All changes were reviewed, tested against the actual codebase, and verified for correctness.