Desenvolvimento de materiais cristalinos para aplicações agroquímicas: dihidroquinolinas com potencial fungicida e caracterização de produtos de degradação de s-triazinas - (:unkn) unknown (Universidade Estadual de Goiás, 2021)
The demand for new agrochemicals has driven an increasing search for the development or improvement of molecules that enable a reduction in the number of doses applied, as alternatives to resistance problems and that can reduce more than one pest for the same crop. In this way, what is sought is the balance between the protection of crops, the increase in productivity, and the reduction of environmental impacts. From the structure-activity relationship, obtained by qualitative models, it is known that certain functional groups are related to specific biological properties even in different chemical species. Similar molecules often have similar properties, and this analysis is important in understanding the biological response caused by the presence of a specific chemical group. Based on this perspective, the molecular structure of ten synthesized dihydroquinolines was elucidated using X-ray diffraction. Then, to understand the structure-activity relationship, machine learning methods were used in this set that predicted these molecules as potentially fungicidal agents. It was possible to map the molecular regions and discern which characteristics were decisive for such activity. Also, an important new metabolic, resulting from the degradation of the herbicides ametrine and atrazine, was structurally elucidated in the form of four salts, using the same technique used for dihydroquinolines. In this way, this thesis points to dihydroquinolines as a promising class, and open to future studies, for the development of new agrochemical agents, in addition to providing structural insights that allow broadening the understanding of herbicide degradation kinetics, with a methodology that can be replicated for other agrochemicals.
Development of crystalline materials for agrochemical applications: dihydroquinolines with fungicidal potential and characterization of s-triazine degradation products
Keywords: Dihidroquinolinas; Machine learning; Herbicidas; Cristalografia; Dihidroquinolines; Machine learning; Herbicides; Crystallography; BIOQUIMICA::BIOLOGIA MOLECULAR; CIENCIAS AGRARIAS