The spread of a novel coronavirus SARS-Cov-2 and a resulting COVID19 disease in late 2019 has transformed into a worldwide pandemic and has effectively brought the world to a halt. Proteases 3CL pro and PL pro , responsible for proteolysis of new virions, represent vital inhibition targets for the COVID19 treatment. Herein, we report an in silico docking study of more than 860 COVID19-related compounds from the PubChem database. Molecular dynamic simulations were carried out to validate the conformation stability of compound-ligand complexes with best docking scores. The MM-PBSA approach was employed to calculate binding free energies. The comparison with ca. 50 previously reported potential SARS-CoV-2’s proteases inhibitors show a number of new compounds with excellent binding affinities. Anti-inflammatory drugs Montelukast, Ebastine and Solumedrol, the anti-migraine drug Vazegepant or the anti-MRSA pro-drug TAK-599, among many others, all show remarkable affinities to 3CL pro and with known side effects present candidates for immediate clinical trials. This study reports thorough docking scores summary of COVID19-related compounds found in the PubChem database and illustrates the asset of computational screening methods in search for possible drug-like candidates. Several yet-untested compounds show affinities on par with reported inhibitors and warrant further attention. Furthermore, the submitted work provides readers with ADME data, ZINC and PubChem IDs, as well as docking scores of all studied compounds for further comparisons.

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Here we present three distinct machine learning (ML) approaches (TensorFlow, XGBoost, and SchNetPack) for docking score prediction. AutoDock Vina is used to evaluate the inhibitory potential of ZINC15 in‐vivo and in‐vitro‐only sets towards the SARS‐CoV‐2 main protease. The in‐vivo set (59 884 compounds) is used for ML training (max. 80%), validation (5%), and testing (15%). The in‐vitro‐only set (174 014 compounds) is used for the evaluation of prediction capability of the trained ML models. Contributions to the prediction error are analyzed with respect to compounds' charge, number of atoms, and expected inhibitory potential (docking score). Methods for the prediction error estimation of new compounds are considered, yet critically rejected. The ML input weighted with respect to the desired property (i.e., low docking score) in the machine learning models shows to be a promising option to improve the ML performance. Proposed models provide significant reduction in number of intriguing compounds that need to be investigated.

Polyphenolic compounds such as flavonoids are closely linked with therapeutical approaches in oxidative stress related diseases mainly because of their antioxidant and metal binding properties. The formation of metal ion...