Applying computer simulations in battling with COVID-19, using pre-analyzed molecular and chemical data to face the pandemic

© 2020 Published by Elsevier Ltd..

Coronavirus disease 2019 (COVID-19) has made many concerns for healthcare services especially, in finding useful therapeutic(s). Despite the scientists' struggle to find and/or creating possible drugs, so far there is no treatment with high efficiency for the disease. During the pandemic, researchers have performed some molecular analyses to find potential therapeutics out of both the natural and synthetic available medicines. Computer simulations and related data have shown a significant role in drug discovery and development before. In this field, antiviral drugs, phytochemicals, anti-inflammatory agents, etc. were essential groups of compounds tested against COVID-19, using molecular modeling, molecular dynamics (MD), and docking tools. The results indicate promising effects of such compounds to be used in further experimental and clinical trials; Chloroquine, Chloroquine-OH, and Umifenovir as viral entry inhibitors, Remdesivir, Ribavirin, Lopinavir, Ritonavir, and Darunavir as viral replication inhibitors, and Sirolimus are the examples, which were tested clinically on patients after comprehensive assessments of the available data on molecular simulation. This review summarizes the outcomes of various computer simulations data in the battle against COVID-19.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:21

Enthalten in:

Informatics in medicine unlocked - 21(2020) vom: 20., Seite 100458

Sprache:

Englisch

Beteiligte Personen:

Khazeei Tabari, Mohammad Amin [VerfasserIn]
Khoshhal, Hooman [VerfasserIn]
Tafazoli, Alireza [VerfasserIn]
Khandan, Mohanna [VerfasserIn]
Bagheri, Abouzar [VerfasserIn]

Links:

Volltext

Themen:

COVID-19
Computer simulation
Journal Article
Molecular docking
Review
SARS-CoV-2

Anmerkungen:

Date Revised 17.12.2020

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1016/j.imu.2020.100458

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM316715050