Computational predictions for multi-target drug design

Abstract

Computational techniques have proven to be an essential tool in modern drug discovery research. These tools offer powerful methods for prediction of ligand–receptor interaction events at atomic details, without attempting exhaustive experimental setup. Single ligand–single target strategies for the discovery of new drug molecules have become outdated due to the factors like drug resistance, increased side effects, reduced efficacy, etc., in addition to the involvement of long time period for validation of a new molecule by toxicology and pharmacokinetic studies. Multi-target drug designing approach can offer a paradigm shift for alternative usage of known drugs for complex diseases. These approaches combine knowledge of complex disease networks, chemical and physical characteristics of drugs, and biological receptors. With the availability of advanced computational resources, a number of tools have been developed that help in the identification of new and multiple targets for the already known or new drugs. In the present chapter, an attempt has been made to highlight the current state-of-the-art methodologies used in multi-target identification for therapeutic effects of known drugs or new drug candidates.