Predicting the Biological Activities Through QSAR Analysis and Docking-Based Scoring

Abstract

Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.