Development of Kinase‐Centric Drugs: A Computational Perspective

Abstract

Kinases are involved in signal transduction, physiological responses, and diseases like cancer, neurological disorders, and autoimmune disorders, making them popular drug targets. Since Gleevec was approved to treat chronic myelogenous leukemia, several FDA‐approved small‐molecule drugs have been created. Kinases were initially "undruggable". Several FDA‐approved small‐molecule drugs have been released recently. Few medications target allosteric sites, but most target ATP‐binding sites. The catalytic domain has high structural and sequence conservation among family kinases. ATP‐binding inhibitors can bind off‐target. Kinases and inhibitors often have intricate connections due to similar sequences and structures. Understanding target selectivity for kinase inhibitors is crucial for drug design and development. Several experimental methodologies profile small molecule kinase selectivities to produce novel inhibitors with the appropriate selectivity. Experimental methods are costly, time‐consuming, and kinase limited. The researchers applied computational methods to overcome these barriers in treatment design and development. Over the last few decades, many computational methods have been developed to supplement experimental data or predict kinase inhibitor efficacy and selectivity. This paper discusses current theoretical/computational breakthroughs in kinase inhibitor design with the necessary selectivity and optimization.