Exploring activity landscapes with extended similarity: is Tanimoto enough?

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Understanding structure-activity relationships (SAR) is crucial for effective drug discovery.
• Activity cliffs can significantly hinder design progress and compromise machine learning model accuracy.
• Analyzing large chemical datasets requires efficient computational tools.

Purpose of the Study:

• To demonstrate the utility of n-ary indices for rapid and efficient quantification of structure-activity landscapes.
• To showcase the application of a novel medoid algorithm for optimizing similarity measures and SAR rankings.
• To evaluate these methods on diverse, pharmaceutically relevant compound datasets.

Main Methods:

• Application of n-ary indices for SAR landscape analysis.
• Utilizing a recently developed medoid algorithm for correlation optimization.
• Testing across 10 diverse compound datasets using multiple chemical fingerprints and similarity metrics.

Main Results:

• N-ary indices provide a rapid and efficient method for analyzing large-scale structure-activity landscapes.
• The medoid algorithm effectively identifies optimal correlations between similarity measures and SAR rankings.
• The combined approach demonstrated robust applicability across various datasets and structural representations.

Conclusions:

• N-ary indices and medoid algorithms are valuable tools for navigating complex chemical space in drug discovery.
• These methods enhance the analysis of activity cliffs and improve the predictive power of computational models.
• Efficient SAR landscape analysis is imperative for accelerating the identification of novel therapeutic agents.