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

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 3CLpro (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.