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An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19.

TLDR
In this paper, the authors highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery.
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.

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Journal ArticleDOI

Drug Design by Pharmacophore and Virtual Screening Approach

TL;DR: The procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature are described.
Journal ArticleDOI

Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates

TL;DR: This work presents the findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19, seven of which have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in CO VID-19 treatment.
Journal ArticleDOI

SARS-CoV-2 Non-Structural Proteins and Their Roles in Host Immune Evasion

TL;DR: The functions and characteristics of SARS-CoV-2 NSPs that confer host immune evasion are summarized and the related potential therapeutic strategies for controlling the COVID-19 pandemic are discussed.
References
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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI

A Novel Coronavirus from Patients with Pneumonia in China, 2019.

TL;DR: Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily, which is the seventh member of the family of coronaviruses that infect humans.
Journal ArticleDOI

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.
Journal ArticleDOI

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

TL;DR: AutoDock4 incorporates limited flexibility in the receptor and its utility in analysis of covalently bound ligands is reported, using both a grid‐based docking method and a modification of the flexible sidechain technique.
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How covid 19 made the rise for computer aided drug design?

The severity of COVID-19 and the lack of approved therapeutic agents have increased the demand for computer-aided drug design in the search for effective drugs.