A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase

dc.creatorMachado, Lucas A.
dc.creatorKrempser, Eduardo
dc.creatorGuimarães, Ana Carolina Ramos
dc.date2023-01-05T18:31:28Z
dc.date2023-01-05T18:31:28Z
dc.date2022
dc.date.accessioned2023-09-26T22:24:23Z
dc.date.available2023-09-26T22:24:23Z
dc.identifierMACHADO, Lucas A.; KREMPSER, Eduardo; GUIMARÃES, Ana Carolina Ramos. A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase. Frontiers in Drug Discovery. v.2, 954911, p. 1 - 13, Oct. 2022.
dc.identifier2674-0338
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/56334
dc.identifier10.3389/fddsv.2022.954911
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8878847
dc.descriptionHIV-1 integrase is an essential enzyme for the HIV-1 replication cycle, and currently, integrase inhibitors are in the first line of treatment in many guidelines. Despite the discovery of new inhibitors, including a new class of molecules with different mechanisms of action, resistance is still a relevant problem, and adding new options to the therapeutic arsenal to fight viral resistance is a Sisyphean task. Because of the difficulty and cost of in vitro screenings, machine learningdriven ligand-based virtual screenings are an alternative that can not only cut costs but also use valuable information about active compounds with yet unknown mechanisms of action. In this work, we describe a thorough model exploration and hyperparameter tuning procedure in a dataset with class imbalance and show several models capable of distinguishing between compounds that are active or inactive against the HIV-1 integrase. The best of the models was then used to screen the natural product atlas for active compounds, resulting in a myriad of molecules that share features with known integrase inhibitors. Here we also explore the strengths and shortcomings of our models and discuss the use of the applicability domain to guide in vitro screenings and differentiate between the “predictable” and “unknown” regions of the chemical space.
dc.formatapplication/pdf
dc.languageeng
dc.publisherFrontiers Media
dc.rightsopen access
dc.subjectAprendizado de máquina
dc.subjectHIV-1
dc.subjectIntegrar
dc.subjectCompostos naturais
dc.subjectInibição
dc.subjectMachine learning
dc.subjectHIV-1
dc.subjectIntegrase
dc.subjectNatural compounds
dc.subjectInhibition
dc.titleA machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
dc.typeArticle


Este ítem pertenece a la siguiente institución