Iterative DeepSARM modeling for compound optimization

Abstract

The Structure-Activity Relationship (SAR) Matrix (SARM) method systematically extracts structurally related compound series from any source and organizes these series in a unique data structure formed by matrices similar to R-group tables from medicinal chemistry. In addition, the SARM method generates virtual analogues for structurally organized series that consist of new combinations of existing core structures and R-groups. For active compounds, SARMs visualize SAR patterns and aid in compound design. The SARM methodology and data structure was integrated with a recurrent neural network architecture to further expand the compound design capacity with deep generative models, leading to the DeepSARM approach. Herein, we present an extension of the DeepSARM framework for compound optimization termed iterative DeepSARM (iDeepSARM), which involves multiple iterations of deep generative modeling and fine-tuning to obtain increasingly likely active compounds for targets of interest. Hence, iDeepSARM adds computational hit-to-lead and lead optimization capability to the DeepSARM framework. In addition to detailing methodological features and calculation protocols, an exemplary compound design application is reported to illustrate the iDeepSARM approach.