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计算机辅助改进具有抗真菌特性和有限除草活性的磺酰脲类药物:在饲料保存中的潜在应用。

Computer-Assisted Improvement of Sulfonylureas with Antifungal Properties and Limited Herbicidal Activity: Potential Application in Forage Conservation.

机构信息

Departamento de Química, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais 37200-900, Brazil.

出版信息

J Agric Food Chem. 2022 Mar 16;70(10):3321-3330. doi: 10.1021/acs.jafc.1c07352. Epub 2022 Mar 1.

Abstract

This work reports studies at the molecular level of a series of modified sulfonylureas to determine the chemophoric sites responsible for their antifungal and herbicidal activities. For forage conservation, high antifungal potency and low phytotoxicity are required. A molecular modeling study based on multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) was performed to model these properties, as well as to guide the design of new agrochemical candidates. As a result, the MIA-QSAR models were reliable, robust, and predictive; for antifungal activity, the averages of the main validation parameters were = 0.936, = 0.741, and = 0.720, and for herbicidal activity, the model was very predictive ( = 0.981 and = 0.944). From the interpretation of the MIA-plots, 46 novel sulfonylureas with likely improved performance were proposed, from which 9 presented promising calculated selectivity indexes. Docking studies were performed to validate the QSAR predictions and to understand the interaction mode of the proposed ligands with the acetohydroxyacid synthase enzyme.

摘要

本工作在分子水平上对一系列修饰的磺酰脲类化合物进行了研究,以确定其抗真菌和除草活性的化学基团。对于饲料保存,需要高抗真菌活性和低植物毒性。应用于定量构效关系(MIA-QSAR)的基于多元图像分析的分子建模研究用于对这些性质进行建模,并指导新的农用化学品候选物的设计。结果表明,MIA-QSAR 模型可靠、稳健且具有预测性;对于抗真菌活性,主要验证参数的平均值分别为 = 0.936、 = 0.741 和 = 0.720,对于除草活性,该模型具有很好的预测性( = 0.981 和 = 0.944)。通过 MIA-图的解释,提出了 46 种具有潜在改进性能的新型磺酰脲类化合物,其中 9 种具有有希望的计算选择指数。进行对接研究以验证 QSAR 预测,并了解所提出的配体与乙酰羟酸合酶的相互作用模式。

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