Computational multi-target drug design

Abstract

Multi-target (mt) therapy is an attractive approach as well as a challenging task in drug discovery research and pharmaceutical industry. The multi-target drug design strategy is an opportunity to find new drugs for the treatment of two or more targets simultaneously. Advanced characterization of bioactive molecules, computational science, and molecular biology have contributed to planning of new bioactive compounds and evaluating different features of multi-targeted drugs. Computational methods have different roles in drug candidate searching, analysis, and prediction in this field. Here, we discuss several in silico methodologies and computer-aided drug design (CADD) as structure-activity relationship (SAR), quantitative SAR (QSAR), pharmacophore modeling, and molecular docking in the process of drug discovery in the field of multi-targeted drugs (MTDs). Computational efficiency of each method has been stated in relation to their key strength and weakness. These capacities for binding affinity prediction are rationally effective with promising potential in easing drug discovery directed at selective multiple targets.