Abstract
The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.
Highlights
Drug discovery is a lengthy process that takes around 10-15 years [1] and costs up to 2.558 billion USD for a drug to reach the market [2]
The availability of complete genome sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the elucidation of the viral protein structures through X-ray crystallography, nuclear magnetic resonance (NMR), electron microscopy, and homology modelling approach have allowed the identification of inhibitor drugs against the essential therapeutic drug targets of COVID-19
The results showed that deep learning models were true which can accurately predict the ADMET properties compared to traditional machine learning methods
Summary
Drug discovery is a lengthy process that takes around 10-15 years [1] and costs up to 2.558 billion USD for a drug to reach the market [2]. CADD can be broadly divided into structure-based and ligand-based drug design approaches, both have been widely used in the drug discovery process in the identification of suitable lead molecules. Computer-aided drug design has a large number of success stories and continues to play a vital role in the drug discovery process [10]. In this regard, the approach has been utilized in proposing drug candidates against coronavirus disease 2019 (COVID-19). The availability of complete genome sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the elucidation of the viral protein structures through X-ray crystallography, nuclear magnetic resonance (NMR), electron microscopy, and homology modelling approach have allowed the identification of inhibitor drugs against the essential therapeutic drug targets of COVID-19. This review article provides useful insights into some of the common in silico methods used in CADD and how these methods have been currently used and can be of help in the drug discovery process of COVID-19
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