MolScope: A Transparent Representation-Aware Workflow Toolkit for Reproducible Medicinal-Chemistry Triage

Area of Science:

• Medicinal Chemistry
• Cheminformatics
• Computational Drug Discovery

Background:

• Medicinal chemistry reviews often rely on fragmented data sources like notebooks and spreadsheets.
• This fragmentation obscures critical structure normalization choices and blurs the line between experimental data and heuristic decisions.

Purpose of the Study:

• To introduce MolScope, an open-source Python toolkit designed to streamline medicinal chemistry workflows.
• To convert diverse molecular structure collections and assay data into a standardized, canonical format for improved analysis and decision-making.

Main Methods:

• MolScope processes molecular structure collections and assay data.
• It generates a canonical chemistry table, category-aware summaries, and actionable reports (HTML, Markdown, CSV).
• The workflow explicitly defines representation policies and preserves data provenance.

Main Results:

• Demonstrated reproducible end-to-end execution using frozen datasets.
• Showcased auditable handling of chemical variations including salts, tautomers, stereo ambiguity, and charge states.
• Provided a framework for comparative campaign support through a round-review example.

Conclusions:

• MolScope offers a robust solution for organizing and analyzing medicinal chemistry data.
• The toolkit enhances transparency, reproducibility, and auditability in drug discovery workflows.
• It facilitates efficient data management and decision-making for medicinal chemistry campaigns.