molclaw-mol-complexity-metrics

To calculate molecular complexity metrics from SMILES strings, you provide the list as input to receive a structured output containing per-molecule scores for molecular_complexity, aromatic_proportion, and asphericity.

Molecular complexity is used in drug discovery to compare a library of molecules and identify high-complexity or niche candidates. The resulting metrics enable downstream filtering and ranking in cheminformatics workflows.

Yes, you can process a list of SMILES strings at once. The tool processes each SMILES individually and returns a structured metrics list aligned with your input, allowing direct comparison across a molecule library.

The best way to filter molecules by asphericity and aromatic proportion is to compute these descriptors directly from your SMILES list. The tool outputs a structured metrics list that can be integrated into filtering and ranking workflows.

No external cheminformatics libraries are required as dependencies. The tool directly accepts a SMILES list as input and computes the molecular_complexity, aromatic_proportion, and asphericity metrics internally.