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Machine learning-based virtual screening, molecular docking, drug-likeness, pharmacokinetics and toxicity analyses to identify new natural inhibitors of the glycoprotein spike (S1) od SARS-CoV-2

2023-03Journal article Open access
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dc.contributor.authorCobre, Alexandre
dc.contributor.authorBöger, Beatriz
dc.contributor.authorFachi, Mariana
dc.contributor.authorEhrenfried, Carlos
dc.contributor.authorStremel, Dile
dc.contributor.authorDe Melo, Eduardo
dc.contributor.authorTonin, Fernanda
dc.contributor.authorPontarolo, Roberto
dc.date.accessioned2023-05-18T10:17:21Z
dc.date.available2023-05-18T10:17:21Z
dc.date.issued2023-03
dc.description.abstractTo identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed a greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15, and NBC27 had better results in ADMET predictions. These had a similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCobre AF, Böger B, Fachi MM, Ehrenfried CA, Stremel DP, Tonin FS, et al. Machine learning-based virtual screening, molecular docking, drug-likeness, pharmacokinetics and toxicity analyses to identify new natural inhibitors of the glycoprotein spike (S1) od SARS-CoV-2. Quim Nova. 2023;46(5):450-9.pt_PT
dc.identifier.doi10.21577/0100-4042.20230038pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/16071
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://s3.sa-east-1.amazonaws.com/static.sites.sbq.org.br/quimicanova.sbq.org.br/pdf/AR2022-0263.pdfpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectCOVID-19pt_PT
dc.subjectSARS-CoV-2pt_PT
dc.subjectIn silicopt_PT
dc.subjectSpike glycoproteinpt_PT
dc.subjectTreatmentpt_PT
dc.titleMachine learning-based virtual screening, molecular docking, drug-likeness, pharmacokinetics and toxicity analyses to identify new natural inhibitors of the glycoprotein spike (S1) od SARS-CoV-2pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage459pt_PT
oaire.citation.issue5pt_PT
oaire.citation.startPage450pt_PT
oaire.citation.titleQuímica Novapt_PT
oaire.citation.volume46pt_PT
person.familyNameTonin
person.givenNameFernanda
person.identifier.ciencia-idD01C-C700-9411
person.identifier.orcid0000-0003-4262-8608
person.identifier.ridO-2050-2017
person.identifier.scopus-author-id56085115800
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication61ded30e-ecec-4b3e-b953-2293e080ebdd
relation.isAuthorOfPublication.latestForDiscovery61ded30e-ecec-4b3e-b953-2293e080ebdd

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