HierS: hierarchical scaffold clustering using topological chemical graphs

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• High-throughput screening (HTS) generates large datasets requiring efficient analysis.
• Compound clustering is crucial for managing structural diversity and identifying key scaffolds.
• Existing methods may not intuitively capture hierarchical structural relationships.

Purpose of the Study:

• To introduce HierS, a novel ring-based algorithm for intuitive compound clustering.
• To enable efficient analysis of HTS results by identifying and organizing molecular scaffolds.
• To facilitate structurally directed navigation and selection of compound libraries.

Main Methods:

• Development of an exhaustive, recursive, ring-based algorithm (HierS).
• Identification of all ring-delimited substructures (scaffolds) within compound sets.
• Establishment of hierarchical structural relationships between identified scaffolds.
• Integration into a web-based interface with activity histograms for interactive analysis.

Main Results:

• HierS rapidly identifies scaffolds and establishes their hierarchical relationships.
• The algorithm enables identification of over-represented structural features for diversity management.
• Interactive analysis is supported through a web interface coupling biological data to scaffolds.
• Prioritization of compounds for follow-up studies based on structural and activity data.

Conclusions:

• HierS provides an intuitive and efficient approach to compound clustering for HTS analysis.
• The hierarchical scaffold network aids in navigating and managing compound library diversity.
• The system facilitates the identification of promising compounds for further investigation, overcoming practical limitations.