Accelerating natural product discovery

• Grant number: 22/08333-8
• Support Opportunities: Regular Research Grants
• Duration: January 01, 2023 - December 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Organic Chemistry
• Convênio/Acordo: DAAD
• Principal Investigator: Vanderlan da Silva Bolzani
• Grantee: Vanderlan da Silva Bolzani
• Principal researcher abroad: Edgard Luiz Marx
• Institution abroad: Leipzig University of Applied Sciences, Germany
• Host Institution: Instituto de Química (IQ). Universidade Estadual Paulista (UNESP). Campus de Araraquara. Araraquara , SP, Brazil
• Associated researchers: Adriano Defini Andricopulo ; João Victor da Silva e Silva ; Marilia Valli
• Associated research grant: 20/11967-3 - Sustainable use of Brazilian biodiversity: using Linked Data for natural product discovery, AP.R

Abstract

Brazil is a country with one of the greatest bio-diversities in the world, accounting for ca. 10% of all living species. It comprises six terrestrial biomes, the Amazonian rainforest, Caatinga, Cerrado (Savanna), Atlantic forest, Pantanal and Pampas. This extraordinary biodiversity and its chemical diversity could be used for the development of bioproducts, including pharmaceuticals, cosmetics, food supplements, and agricultural pesticides. As an initial effort with in the scope of DFG/FAPESP funded Sustainable use of Brazilian Biodiversity (DINOBBIO), a Database of Natural Products from Brazilian biodiversity was initially developed by the partner institutions with the objective of organizing its chemical, biological and pharmacological information. This database presently contains data of 2,218 compounds, an estimation of 5% of the published information on natural products isolated/identified from species collected in the Brazilian territory. Within this proposal, we would like to intensify the DFG/FAPESP DINOBBIO project collaboration between the Institute for Applied Informatics (INFAI/AKSW), the University of São Paulo (USP) and the São Paulo State University (UNESP) by accelerating the research on discovering natural products empowered by Semantic Web Technologies and Machine Learning using Brazilian biodiversity. (AU)