Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus.
Biophys Chem
; 288: 106854, 2022 09.
Article
in English
| MEDLINE | ID: covidwho-1906814
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
SARS-CoV-2
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Biophys Chem
Year:
2022
Document Type:
Article
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