Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus

• Published in: Biophysical chemistry 2022-09, Vol.288, p.106854-106854, Article 106854
• Main Authors: Jablonský, Michal, Štekláč, Marek, Majová, Veronika, Gall, Marián, Matúška, Ján, Pitoňák, Michal, Bučinský, Lukáš
• Format: Article
• Language: English
• Subjects: Antiviral Agents - pharmacology; COVID-19; Machine Learning; Molecular Docking Simulation; Molecular Dynamics Simulation; Peptide Hydrolases; Plant Bark; Protease Inhibitors - pharmacology; SARS-CoV-2
• ISSN: 0301-4622; 1873-4200
• DOI: 10.1016/j.bpc.2022.106854

Molecular docking of 234 unique compounds identified in the softwood bark (W set) is presented with a focus on their inhibition potential to the main protease of the SARS-CoV-2 virus 3CL (6WQF). The docking results are compared with the docking results of 866 COVID19-related compounds (S set). Furthermore, machine learning (ML) prediction of docking scores of the W set is presented using the S set trained TensorFlow, XGBoost, and SchNetPack ML approaches. Docking scores are evaluated with the Autodock 4.2.6 software. Four compounds in the W set achieve a docking score below -13 kcal/mol, with (+)-lariciresinol 9'-p-coumarate (CID 11497085) achieving the best docking score (-15 kcal/mol) within the W and S sets. In addition, 50% of W set docking scores are found below -8 kcal/mol and 25% below -10 kcal/mol. Therefore, the compounds identified in the softwood bark, show potential for antiviral activity upon extraction or further derivatization. The W set molecular docking studies are validated by means of molecular dynamics (five best compounds). The solubility (Log S, ESOL) and druglikeness of the best docking compounds in S and W sets are compared to evaluate the pharmacological potential of compounds identified in softwood bark.