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Harnessing the Capability of CADD Methods in the Prediction of Anti-COVID Drug Likeliness

Harnessing the Capability of CADD Methods in the Prediction of Anti-COVID Drug Likeliness
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Author(s): Shubhra Chaturvedi (Institute of Nuclear Medicine and Allied Sciences, India), Vishaka Chaudhary (Institute of Nuclear Medicine and Allied Sciences, India), Tina Klauss (Université de Bordeaux, France), Philippe Barthélémy (ChemBioPharm, France) and Anil Kumar Mishra (Institute of Nuclear Medicine and Allied Sciences, India)
Copyright: 2021
Pages: 19
Source title: Strategies to Overcome Superbug Invasions: Emerging Research and Opportunities
Source Author(s)/Editor(s): Dimple Sethi Chopra (Punjabi University, India) and Ankur Kaul (Institute of Nuclear Medicine and Allied Sciences, India)
DOI: 10.4018/978-1-7998-0307-2.ch011

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Abstract

The COVID-19 pandemic has claimed many lives and added to the social, economic, and psychological distress. The contagious disease has quickly spread to almost 200 countries following the regional outbreak in China. As the number of infected populations increases exponentially, there is a pressing demand for anti-COVID drugs and vaccines. Virtual screening provides possible leads while extensively cutting down the time and resources required for ab-initio drug design. The chapter aims to highlight the various computer-aided drug design methods to predict an anti-COVID drug molecule.

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