Chapter 29 - 3D Quantitative Structure–Property Relationships

Abstract

3D QSAR methods have proven useful when optimizing series of compounds in drug development scenarios or agrochemical research applications. One of the most important goals in computer-assisted ligand optimization is to find a correlation between structural features of molecules and their biological activity, that is, their ability to bind to specific macromolecular targets. Traditional QSAR has been used successfully for optimizing congeneric series of lead structures, which has led to the development of several commercial drugs. However, this approach can have severe limitations. This chapter provides an overview on 3D QSAR methods and reports how to use them optimally in the context of medicinal chemistry. It focuses primarily on CoMFA as it has been the most successful and widely used approach in this field. The aim of any 3D QSAR study is to determine a correlation between a compound's molecular field and its biological activity, that is, the affinity to its target. The quality and reliability of any 3D QSAR model is strongly dependent on the careful examination of each step within 3D QSAR analysis. The initial alignment of compounds in the series is widely recognized as one of the most difficult and time-consuming steps in 3D QSAR analyses. One of the trends in drug discovery research over the last decade has been to screen large numbers of compounds in high-throughput assays. The alignment of independent molecular field descriptors based methods might influence the situation and push 3D QSAR forward into the world of high-throughput in silico prediction tools.