In silico screening of the drugbank database to search for possible drugs against sars-cov-2

Sebastián A. Cuesta, José R. Mora, Edgar A. Márquez

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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, Q2 LOO = 0.854, and Q2 ext = 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.

Original languageEnglish
Article number1100
Issue number4
StatePublished - 2 Feb 2021


  • Docking analysis
  • DrugBank
  • Molecular dynamics
  • QSAR
  • SARS-CoV-2


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