On exploring structure-activity relationships

Abstract

Understanding structure-activity relationships (SARs) for a given set of molecules allows one to rationally explore chemical space and develop a chemical series optimizing multiple physicochemical and biological properties simultaneously, for instance, improving potency, reducing toxicity, and ensuring sufficient bioavailability. In silico methods allow rapid and efficient characterization of SARs and facilitate building a variety of models to capture and encode one or more SARs, which can then be used to predict activities for new molecules. By coupling these methods with in silico modifications of structures, one can easily prioritize large screening decks or even generate new compounds de novo and ascertain whether they belong to the SAR being studied. Computational methods can provide a guide for the experienced user by integrating and summarizing large amounts of preexisting data to suggest useful structural modifications. This chapter highlights the different types of SAR modeling methods and how they support the task of exploring chemical space to elucidate and optimize SARs in a drug discovery setting. In addition to considering modeling algorithms, I briefly discuss how to use databases as a source of SAR data to inform and enhance the exploration of SAR trends. I also review common modeling techniques that are used to encode SARs, recent work in the area of structure-activity landscapes, the role of SAR databases, and alternative approaches to exploring SAR data that do not involve explicit model development.

Figure 1

An example of a glowing molecule representation, developed by Optibrium. The color coding corresponds to the influence of the structural feature on the predicted property (red for a negative influence, blue for a positive influence). Image modified, with permission, from http://www.optibrium.com/community/faq/glowing-molecule.

Figure 2

A schematic diagram of R-group QSAR workflow that can be used to rank scaffolds (i.e., chemical series) in terms of whether they exhibit an SAR or not.

Figure 3

Screenshots of the Fragment Activity Profiler. The top figure highlights the single scaffold view that displays activity profiles for a given scaffold versus individual targets (at a specified level of the ChEMBL target hierarchy. The bottom figure shows the pairwise view, allowing one to compare activity profiles of two scaffolds simultaneously.