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Identifying 124 new anti-HIV drug candidates in a 37 billion-compound database: an integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulation

2024-07Journal article Embargoed access
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dc.contributor.authorCobre, Alexandre de Fátima
dc.contributor.authorAra, Anderson
dc.contributor.authorAlves, Alexessander Couto
dc.contributor.authorNeto, Moisés Maia
dc.contributor.authorFachi, Mariana Millan
dc.contributor.authorBeca, Laize Botas
dc.contributor.authorTonin, Fernanda
dc.contributor.authorPontarolo, Roberto
dc.date.accessioned2024-08-28T10:02:39Z
dc.date.embargo2026-08
dc.date.issued2024-07
dc.description.abstractRecent data from the World Health Organization reveals that in 2023, 38.8 million people were living with HIV. Within this population, there were 1.5 million new cases and 650 thousand deaths attributed to the disease. This study employs an integrated approach involving QSAR-based machine learning models, molecular docking, and molecular dynamics simulations to identify potential compounds for inhibiting the bioactivity of the CC chemokine receptor type 5 (CCR5) protein, a key entry point for HIV. Using non-redundant experimental data from the CHEMBL database, 40 different machine learning algorithms were trained and the top four models (XGBoost, Histogram gradient Boosting, Light Gradient Boosted Machine, and Extra Trees Regression) were utilized to predict anti-HIV bioactivity for 37 billion compounds in the ZINC-22 database. The screening resulted in the identification of 124 new anti-HIV drug candidates, confirmed through molecular docking and dynamics simulations. The study underscores the therapeutic potential of these compounds, paving the way for further in vitro and in vivo investigations. The convergence of machine learning and experimental findings presents a promising avenue for significant advancements in pharmaceutical research, particularly in the treatment of viral diseases such as HIV. To guarantee the reproducibility of our study, we have made the Python code (Google Collab) and the associated database available on GitHub. You can access them through the following link: GitHub Link: https://github.com/AlexandreCOBRE/codept_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCobre AF, Ara A, Alves AC, Neto MM, Fachi MM, Tonin FS, et al. Identifying 124 new anti-HIV drug candidates in a 37 billion-compound database: an integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulation. Chemometr Intell Lab Syst. 2024;250:105145.pt_PT
dc.identifier.doi10.1016/j.chemolab.2024.105145pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/17630
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0169743924000856pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectHIVpt_PT
dc.subjectCCR5pt_PT
dc.subjectDrug discoverypt_PT
dc.subjectMachine learningpt_PT
dc.subjectMolecular dockingpt_PT
dc.subjectMolecular dynamicspt_PT
dc.titleIdentifying 124 new anti-HIV drug candidates in a 37 billion-compound database: an integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage105145pt_PT
oaire.citation.titleChemometrics and Intelligent Laboratory Systemspt_PT
oaire.citation.volume250pt_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.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication61ded30e-ecec-4b3e-b953-2293e080ebdd
relation.isAuthorOfPublication.latestForDiscovery61ded30e-ecec-4b3e-b953-2293e080ebdd

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