Computer-assisted improvement of Sulfonylureas with antifungal properties and limited herbicidal activity: potential application in forage conservation

• Ano de defesa: 2022
• Autor(a) principal: Faria, Adriana Cássia de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Agroquímica; UFLA; brasil; Departamento de Química
• Palavras-chave em Português: Silagem - Conservação; Sulfonilureias; Aceto-hidroxiácido sintase; Fungicidas; Forragem; Silage - Conservation; Sulfonylureas; Acetohydroxyacid synthase; Fungicides; Forage; Química
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/49494

The conservation of forage for animal feed - silage - requires the use of antifungals as additives to ensure high fermentative efficiency in an anaerobic environment. In this context, new compounds belonging to the class of sulfonylureas have recently been synthesized with the purpose of modifying the chemical structures of analogous and already known substances, improving their antifungal properties and minimizing their herbicidal activities. This work proposes the study at the molecular level of these sulfonylureas, with the objective of finding the chemophoric sites (portions of the molecular structure) responsible for the antifungal activity and for the selectivity (antifungal activity/herbicidal activity) of the molecules to be studied. Accordingly, we employed the computational methodology of molecular modeling MIA-QSAR (Multivariate Image Analysis applied to Quantitative Structure-Activity Relationships), as well as the MIA-Plot tool (to identify the chemophoric sites). The MIAQSAR method was used to find a correlation between the chemical structures of the sulfonylureas with their respective biological properties. As a result, the MIA-QSAR models were reliable, robust, and predictive, i.e. for antifungal activity, the average values for the main validation parameters were r2 = 0.936, q2 = 0.741, and r2 pred = 0.720, whereas for herbicidal activity, the model was predictive (r2 pred = 0.981 and r2 m = 0.944). In this way, and knowing which parts of chemical structures affect the biological properties and how (increasing or decreasing microbiological activity), it was possible to propose 46 new chemical structures with improved properties, from which, 9 presented promising calculated selectivity indexes. Docking studies were carried out in order to validate the QSAR predictions and understanding the mechanisms of action of the compounds under study.