CASE STUDIES ON APPLICATIONS OF COMPUTATIONAL TECHNIQUES IN DRUG DESIGN

Abstract

Computer-aided drug design (CADD) along with Artificial Intelligence (AI) based machine learning technologies have a powerful impact in the field of drug discovery as it can handle vast biological data which in turn reduces the cost and time of drug discovery and development process. Identifying hits through virtual screening and its further optimization for the development of lead molecule through ligand- or structure-based drug designing have played a vital role. Docking and molecular dynamics studies highlighted the binding of ligands with targeted proteins and their binding affinity. ADMET prediction studies prevent the failure of many drugs in clinical trials and thus prevent loss of time and money. The availability of open-source big data has facilitated the screening of vast libraries intending to come up with novel potent and target-specific drugs. Present review has given an overview of the technology used in drug discovery by highlighting some of the case studies.