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 pIC 50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R 2 = 0.897, Q 2 LOO = 0.854, and Q 2 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 pIC 50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC 50 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.
【저자키워드】 SARS-CoV-2, molecular dynamics, QSAR, docking analysis, DrugBank, 【초록키워드】 COVID-19, coronavirus, drugs, docking, protease, Papain-like protease, Coverage, death, RNA-dependent RNA polymerase, target, dataset, molecular, Analysis, molecular orbitals, inhibitors of SARS-CoV-2, Compound, domain, candidate, Papain, DrugBank database, applicability, parameter, biological activity, relationship, FIVE, robust, responsible, performed, reported, suggested, reveal, R 2, was related, 【제목키워드】 drug, Screening,