Abstract
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 pro (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.
Keywords: 3CL(pro); Machine learning; Molecular docking; SARS-CoV-2; Softwood bark.
【저자키워드】 SARS-CoV-2, machine learning, molecular docking, 3CL(pro), Softwood bark., 【초록키워드】 machine learning, molecular docking, SARS-CoV-2 virus, 3CL pro, docking, molecular dynamics, protease, antiviral activity, molecular, compounds, approaches, Molecular docking study, Compound, docking scores, extraction, machine, molecular docking studies, docking score, docking results, solubility, pharmacological, TensorFlow, FIVE, Druglikeness, evaluate, addition, evaluated, unique, docking result, Softwood, the SARS-CoV-2 virus, 【제목키워드】 docking, Compound, the SARS-CoV-2 virus,