IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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
View Sample PDF
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

Purchase

View Harnessing the Capability of CADD Methods in the Prediction of Anti-COVID Drug Likeliness on the publisher's website for pricing and purchasing information.

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.

Related Content

Sharon L. Burton. © 2024. 25 pages.
Laura Ann Jones, Ian McAndrew. © 2024. 24 pages.
Olayinka Creighton-Randall. © 2024. 14 pages.
Stacey L. Morin. © 2024. 11 pages.
N. Nagashri, L. Archana, Ramya Raghavan. © 2024. 22 pages.
Esther Gani, Foluso Ayeni, Victor Mbarika, Abdullahi I. Musa, Oneurine Ngwa. © 2024. 25 pages.
Sia Gholami, Marwan Omar. © 2024. 18 pages.
Body Bottom