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In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2
- Source :
- Molecules, Vol 26, Iss 4, p 1100 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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.
Details
- Language :
- English
- ISSN :
- 14203049
- Volume :
- 26
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Molecules
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.75738541e73e4517b80fffa37afd2d1d
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/molecules26041100