| Author | Machado, Lucas A. | |
| Author | Krempser, Eduardo | |
| Author | Guimarães, Ana Carolina Ramos | |
| Access date | 2023-01-05T18:31:28Z | |
| Available date | 2023-01-05T18:31:28Z | |
| Document date | 2022 | |
| Citation | MACHADO, 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. | en_US |
| ISSN | 2674-0338 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/56334 | |
| Language | eng | en_US |
| Publisher | Frontiers Media | en_US |
| Rights | open access | |
| Subject in Portuguese | Aprendizado de máquina | en_US |
| Subject in Portuguese | HIV-1 | en_US |
| Subject in Portuguese | Integrar | en_US |
| Subject in Portuguese | Compostos naturais | en_US |
| Subject in Portuguese | Inibição | en_US |
| Title | A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase | en_US |
| Type | Article | |
| DOI | 10.3389/fddsv.2022.954911 | |
| Abstract | HIV-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. | en_US |
| Affilliation | Faculty of Exact and Natural Sciences, University of Buenos Aires. Buenos Aires, Argentina. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Plataforma Institucional para a Biodiversidade e Saúde da Vida Selvagem. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Genômica Funcional e Bioinformática. Rio de Janeiro, RJ, Brasil. | en_US |
| Subject | Machine learning | en_US |
| Subject | HIV-1 | en_US |
| Subject | Integrase | en_US |
| Subject | Natural compounds | en_US |
| Subject | Inhibition | en_US |
| xmlui.metadata.dc.subject.ods | 04 Educação de qualidade | |
