In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2.
Molecules
; 26(4)2021 Feb 19.
Article
in English
| MEDLINE | ID: covidwho-1090312
ABSTRACT
Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2 main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
RNA-Dependent RNA Polymerase
/
Cysteine Proteinase Inhibitors
/
Molecular Dynamics Simulation
/
Molecular Docking Simulation
/
Databases, Chemical
/
Coronavirus 3C Proteases
/
SARS-CoV-2
/
COVID-19
Type of study:
Prognostic study
Topics:
Traditional medicine
Language:
English
Journal subject:
Biology
Year:
2021
Document Type:
Article
Affiliation country:
Molecules26041100
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