The use of Artificial Intelligence in predicting Respiratory Syncytial Virus-inhibiting flavonoids

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dc.contributor.authorLopes, B. R.P. [UNESP]
dc.contributor.authorAlbertini, T. T. [UNESP]
dc.contributor.authorCosta, M. F. [UNESP]
dc.contributor.authorFerreira, A. S. [UNESP]
dc.contributor.authorToledo, K. A. [UNESP]
dc.contributor.authorRocha, J. C. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T13:57:16Z
dc.date.available2023-07-29T13:57:16Z
dc.date.issued2023-01-01
dc.description.abstractHuman Respiratory Syncytial Virus (hRSV) infection results in death and hospitalization of thousands of people worldwide each year. Unfortunately, there are no vaccines or specific treatments for hRSV infections. Screening hundreds or even thousands of promising molecules is a challenge for science. We integrated biological, structural, and physicochemical properties to train and to apply the concept of artificial intelligence (AI) able to predict flavonoids with potential anti-hRSV activity. During the training and simulation steps, the AI produced results with hit rates of more than 83%. The better AIs were able to predict active or inactive flavonoids against hRSV. In the future, in vitro and/or in vivo evaluations of these flavonoids may accelerate trials for new anti-RSV drugs, reduce hospitalizations, deaths, and morbidity caused by this infection worldwide, and be used as input in these networks to determine which parameter is more important for their decision.en
dc.description.affiliationUniversidade Estadual Paulista - UNESP Faculdade de Ciências e Letras Departamento de Ciências Biológicas
dc.description.affiliationUniversidade Estadual Paulista - UNESP Faculdade de Ciências e Letras Laboratório de Matemática Aplicada
dc.description.affiliationUniversidade Estadual Paulista - UNESP Instituto de Biociências Letras e Ciências Exatas Pós-graduação em Microbiologia
dc.description.affiliationUnespUniversidade Estadual Paulista - UNESP Faculdade de Ciências e Letras Departamento de Ciências Biológicas
dc.description.affiliationUnespUniversidade Estadual Paulista - UNESP Faculdade de Ciências e Letras Laboratório de Matemática Aplicada
dc.description.affiliationUnespUniversidade Estadual Paulista - UNESP Instituto de Biociências Letras e Ciências Exatas Pós-graduação em Microbiologia
dc.format.extente270776
dc.identifierhttp://dx.doi.org/10.1590/1519-6984.270776
dc.identifier.citationBrazilian journal of biology = Revista brasleira de biologia, v. 83, p. e270776-.
dc.identifier.doi10.1590/1519-6984.270776
dc.identifier.issn1678-4375
dc.identifier.scopus2-s2.0-85160641550
dc.identifier.urihttp://hdl.handle.net/11449/248914
dc.language.isoeng
dc.relation.ispartofBrazilian journal of biology = Revista brasleira de biologia
dc.sourceScopus
dc.titleThe use of Artificial Intelligence in predicting Respiratory Syncytial Virus-inhibiting flavonoidsen
dc.typeArtigo
unesp.author.orcid0000-0001-9979-6325 0000-0001-9979-6325[2]
unesp.author.orcid0000-0003-1237-0538 0000-0003-1237-0538[3]
unesp.author.orcid0000-0003-0998-650X 0000-0003-0998-650X[4]
unesp.author.orcid0000-0001-7212-6794 0000-0001-7212-6794[5]
unesp.author.orcid0000-0002-0094-2634 0000-0002-0094-2634[6]

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Artigos - Biologia - IBILCE