Computational characterization of SAR microenvironments in high-throughput screening data

Abstract

Purpose: A computational approach is described to analyze structure–activity relationship (SAR) information contained in compound and screening data sets. The methodology is designed to explore SAR information in a systematic and compound-centric manner in order to aid in the selection of hits from high-throughput screening (HTS) data. Methods: Chemical neighborhood graphs integrate a graphical representation of the chemical environment of each active compound in a data set with the potency distribution within its neighborhood and information from a quantitative SAR analysis function. Environments are systematically generated and ranked by SAR information content. From these environments, key compounds and compound series can be selected. Results: The methodology is described in detail. In addition, the application to four screening data sets is reported, revealing different SAR characteristics. A number of different examples of compound environments are presented and discussed that have varying SAR information