Repositori URV

Identificador: TDX:925

Handle: https://hdl.handle.net/20.500.11797/TDX925

Autores:
Sala Argüello, Esther

Resumen:
One of the main challenges in nutrigenomics is improving the efficiency of the selection (which is currently time consuming and expensive) of new bioactive compounds in order to expedite the development of new functional foods. Computational techniques, such as virtual screening, may play an essential role in accelerating the early stages of the discovery of new bioactive substances by efficiently searching for compounds that could activate or inhibit a known target. So, by modulating specific target functions in the body, molecules that act as IKK-2 or 11β-HSD1 inhibitors have beneficial physiological effects that can be of interest for preventing, retarding and/or reversing the metabolic syndrome. Because of their anti-inflammatory properties, natural extracts that contain these molecules have a promising role as ingredients in new functional foods. Therefore, this PhD thesis will focus on the development of virtual screening workflows that predict natural products that can inhibit both targets.