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Visualising lead optimisation series using reduced graphs.

Jessica Stacey1, Baptiste Canault2, Stephen D Pickett2

  • 1Information School, University of Sheffield, The Wave, 2 Whitham Road, Sheffield, S10 2AH, UK.

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|April 24, 2025
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Summary
This summary is machine-generated.

This study introduces a new automated method using reduced molecular graph descriptions to represent lead optimization series. This approach effectively groups similar compounds and identifies distinct series, advancing drug discovery analysis.

Keywords:
Lead optimisationReduced graphsSARVisualisation

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Lead optimization (LO) series are traditionally visualized using Markush structures and R-group tables.
  • Existing automated methods like SAR maps struggle with minor core structure variations or multiple scaffolds.
  • This limits the intuitive visualization of structure-activity relationships (SAR).

Purpose of the Study:

  • To develop an automated approach for representing lead optimization series.
  • To overcome limitations of traditional Markush structures and existing automated methods.
  • To provide a more holistic view of lead optimization data.

Main Methods:

  • Utilizing reduced graph descriptions of molecules for series representation.
  • Analyzing a publicly available lead optimization dataset from a drug discovery program.
  • Developing code for generating visualizations of compound series.

Main Results:

  • The method successfully groups compounds with minor substructural differences within the same series.
  • It can differentiate between related but distinct compound series.
  • Visualizations highlight under-explored areas and aid in mapping design ideas.

Conclusions:

  • The reduced graph representation offers an advance over traditional Markush structures and SAR tables.
  • The software provides a holistic view, integrating potentially separate series.
  • This tool enhances the medicinal chemist's ability to analyze and strategize lead optimization efforts.