"Molecular Anatomy": a new multi-dimensional hierarchical scaffold analysis tool.

Area of Science:

• Medicinal Chemistry
• Cheminformatics
• Computational Drug Discovery

Background:

• Molecular scaffolds are crucial for classifying bioactive compounds and linking structures to biological activities.
• Existing methods for scaffold analysis can be rigid and may not capture complex relationships within large datasets.

Purpose of the Study:

• To introduce "Molecular Anatomy," a flexible and unbiased scaffold-based metric for clustering large compound libraries.
• To develop a novel approach for identifying relevant chemical moieties and improving structure-activity relationship (SAR) analysis.

Main Methods:

• Utilizing nine molecular representations at varying abstraction levels.
• Implementing fragmentation rules to create a multi-dimensional network of interconnected molecular frameworks.
• Applying flexible scaffold definitions and multiple pruning rules for robust analysis.

Main Results:

• Demonstrated effectiveness in identifying key chemical moieties and clustering active molecules across different classes.
• Successfully captured significant structure-activity information, particularly in large libraries with many unique compounds (singletons).
• Developed a network visualization protocol for efficient navigation and high-quality SAR analysis.

Conclusions:

• Molecular Anatomy provides a powerful, flexible, and unbiased tool for scaffold-based compound clustering and SAR analysis.
• The approach facilitates the discovery of meaningful chemical relationships within large and complex molecular datasets.
• A freely available web interface (https://ma.exscalate.eu) supports the practical application of this protocol.