In silico-driven identification and structural analysis of nitrodihydroquinolinone pesticide candidates with antifungal activity

Abstract

ABSTRACT In this paper, we performed an in silico-driven design model to synthesize compounds with biological activity. This rational design has the advantage of decreasing the time and the need for experimental tests and, consequently, the cost related to the search for different candidates. In this way, there is a necessity for more studies that look for new molecules or compounds that may be alternatives to replace the most harmful chemicals for safer options. To contribute to filling this gap, we started an investigation looking for molecules with bioactive potential using a previously developed machine learning model. Leading us to the synthesis, spectroscopic and structural characterization of (E)-2-(4-chlorophenyl)-3-(4-nitrobenzylidene)-1-(phenylsulfonyl)-2,3-dihydroquinolin-4(1H)-one. Furthermore, considering the predicted biological profile, one of its isomers was incorporated in this study and submitted to experimental validation. The in vitro results indicated that the compounds have antifungal activity against Aspergillus niger in the same range of positive controls. Moreover, both compounds crystallized in the P21/n space group, and their packing is mainly ruled by C–H⋯O interactions. Lastly, we hope that findings can be used as a starting point for new studies where the structural and biological knowledge of dihydroquinolinones leads to the designing of less toxic or nontoxic analogs antifungal agents by changing undesirable fragments by desirable ones in the molecular skeleton.