SARgate: a structure-bioactivity analyser for navigating chemical space and mining SAR trends

Area of Science:

• Medicinal Chemistry
• Cheminformatics
• Computational Chemistry

Background:

• Increasing chemical library sizes necessitate advanced cheminformatics tools for structure-activity relationship (SAR) analysis.
• Existing SAR methods often struggle with diverse chemical series due to rigid scaffold definitions and limited chemotype recognition.
• Identifying SAR trends across partially overlapping chemical structures remains a challenge.

Purpose of the Study:

• To introduce SARgate, an open-source platform for organizing chemical libraries and exploring multi-level SAR.
• To enable flexible R-group decomposition and recognition of chemically related series through generalized core derivation.
• To provide a unified graphical environment for SAR exploration accessible to users of varying computational expertise.

Main Methods:

• Development of SARgate using Python and RDKit, incorporating automated aggregation of Bemis-Murcko scaffolds into generalized cores.
• Integration of dataset curation, scaffold organization, R-group analysis, matched molecular pair analysis (MMPA), stereochemical evaluation, and similarity assessment.
• Visualization of structure-activity landscapes within an interactive workflow.

Main Results:

• SARgate successfully organized diverse chemical libraries into structurally coherent subsets.
• Case studies with AKT1 and IL4I1 inhibitor datasets demonstrated the platform's ability to recover known SAR determinants.
• The tool identified activity-driving substituents, stereochemical constraints, and context-dependent effects.

Conclusions:

• SARgate facilitates mechanistically interpretable medicinal chemistry insights from large-scale bioactivity data.
• The platform enhances the recognition of SAR trends across structurally diverse chemical series.
• SARgate offers a unified and accessible workflow for advanced SAR exploration.