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Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study.

Authors
  • Hatmal, Ma'mon M1
  • Abuyaman, Omar1
  • Taha, Mutasem2
  • 1 Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, PO Box 330127, Zarqa 13133, Jordan. , (Jordan)
  • 2 Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman 11942, Jordan. , (Jordan)
Type
Published Article
Journal
Computational and Structural Biotechnology Journal
Publisher
Elsevier
Publication Date
Jan 01, 2021
Volume
19
Pages
4790–4824
Identifiers
DOI: 10.1016/j.csbj.2021.08.023
PMID: 34426763
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach. © 2021 The Author(s).

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