The SAR Matrix Method and an Artificially Intelligent Variant for the Identification and Structural Organization of Analog Series, SAR Analysis, and Compound Design

The SAR Matrix (SARM) approach was originally conceived for the systematic identification of analog series, their structural organization, and graphical structure‐activity relationship (SAR) analysis. For structurally related series, SARMs also produce virtual candidate compounds. Hence, SARM represents a unique computational approach establishing a direct link between SAR visualization and compound design. The SARM data structure is reminiscent of R‐group tables and hence easily accessible from a chemical perspective, although the underlying algorithmic basis is complex. The SARM concept has been extended in different ways to further increase its analytical and design capacity. While the efforts were largely driven from a research perspective, they have also increased the utility for practical applications. Among others, extensions include approaches for SARM‐based compound activity prediction, the generation of a large SARM database for analog searching, and the design of a deep learning architecture for advanced analog design taking chemical space information for target families into account. Herein, the SARM approach and its extensions are discussed within their scientific context.