Application of artificial intelligence techniques to predict the antiviral activity of flavonoids in relation to the respiratory syncytial virus

• Grant number: 20/08588-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2020
• End date: August 31, 2021
• Field of knowledge: Biological Sciences - Microbiology
• Principal Investigator: José Celso Rocha
• Grantee: Thais Tondato Albertini
• Host Institution: Faculdade de Ciências e Letras (FCL-ASSIS). Universidade Estadual Paulista (UNESP). Campus de Assis. Assis , SP, Brazil

Abstract

Flavonoids are secondary metabolites produced by plants with different functions in the plant kingdom, related from species dispersion to their protection. Studies show that these compounds also have a promising therapeutic potential, as they have anti-inflammatory, antibacterial, antiviral activities, among others. Given the antiviral properties, research indicates that flavonoids act in the inactivity of the respiratory syncytial virus (RSV), due to their potential for interaction with viral particles via interaction with surface proteins involved in the virus's infectivity.Initially manifesting as bronchiolitis, RSV is the main cause of diseases in the lower respiratory tract of children, resulting, in severe cases, hospitalization for pneumonia, which can lead to death. Despite studies in this area, there are still no vaccines for its prevention, only prototypes, and few drugs exist as a form of treatment.In this context, the development of an antiviral drug using natural molecules such as flavonoids can be an important alternative for the treatment of this disease. However, there are hundreds of flavonoids, which would result in great costs (financial, human resources and time) on the laboratories' benches to test their activities. The development of artificial intelligence techniques such as artificial neural networks, associated with the genetic algorithm technique, as proposed in this work, could be an innovative alternative to partially solve this problem, since it would be possible to predict, without great costs and in a short time, the activity of these compounds from biological, chemical and physical input variables, related to flavonoids.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

B. R. P. LOPES; T. T. ALBERTINI; M. F. COSTA; A. S. FERREIRA; K. A. TOLEDO; J. C. ROCHA. O uso da Inteligência Artificial na predição de flavonoides inibidores do Vírus Sincicial Respiratório. Brazilian Journal of Biology, v. 83, 2023-05-26. (20/08588-0)