Improved SAR Models - Exploiting the Target-Ligand Relationships

Abstract

Small organic molecules, by binding to different proteins, can be used to modulate (inhibit/activate) their functions for therapeutic purposes and to elucidate the molecular mechanisms underlying biological processes. Over the decades structure-activity-relationship (SAR) models have been developed to quantify the bioactivity relationship of a chemical compound interacting with a target protein, with advances focussing on the chemical compound representation and the statistical learning methods.

We have developed approaches to improve the performance of SAR models using compound activity information from different targets. The methods developed in the study aim to determine the candidacy of a target to help another target in improving the performance of its SAR model by providing supplemental activity information. Having identified a helping target we also develop methods to identify a subset of compounds that would result in improving the sensitivity of the SAR model.

Identification of helping targets as well as helping compounds is performed using various nearest neighbor approaches using similarity measures derived from the targets as well as active compounds. We also developed methods that involve use of cross-training a series of SVM-based models for identifying the helping set of targets. Our experimental results show that our methods show statistically significant results and incorporate the target-ligand activity relationship well.