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针对Macrophomina phaseolina的新二进制杀菌剂混合物:机器学习驱动的QSAR,交叉预测和分子动力学模拟

Zaniar Rahimi-Soujeh¹, Naser Safaie¹, Sajad Moradi²

¹Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

|October 17, 2024

概括

定量结构-活性关系 (QSAR) 分析确定了控制Macrophomina phaseolina的最佳杀菌剂混合物. 像支持向量回归 (SVR) 和高斯过程回归 (GPR) 这样的机器学习模型在预测联合杀菌剂效应方面被证明是最有效的.

科学领域:

农业科学农业科学
计算化学计算化学
毒理学毒理学毒理学

背景情况:

农药的开发依赖于了解杀剂的相互作用.
马克罗福米纳 (Macrophomina phaseolina) 对作物产量构成重大威胁.
定量结构-活性关系 (QSAR) 有助于预测化学化合物的活性.

研究的目的:

为了研究二进制杀菌剂混合物对Macrophomina phaseolina的综合作用.
开发用于杀菌剂有效性的预测QSAR模型.
建立一个可持续和有效的杀菌剂组合的框架.

主要方法:

使用固定比率射线设计 (FRRD) 产生了六种杀菌剂的75种二元混合物.
应用多重线性回归 (MLR),机器学习 (ML) 和横读 (RA) 方法.
使用支持向量回归 (SVR) 和高斯过程回归 (GPR) 进行QSAR分析.
进行分子动力学 (MD) 模拟以了解相互作用机制.

主要成果:

许多杀菌剂混合物表现出基于度添加 (CA) 和独立作用 (IA) 模型的添加相互作用.
SVR (R2=0.95,Q2LMO=0.81) 和GPR (R2=0.93,Q2LMO=0.81) 是最有效的QSAR模型.
医学药物模拟表明杀菌剂的作用是生理性的,而不是基于配方的.

结论:

开发了强大且经过验证的QSAR模型 (SVR,GPR) 来预测杀真菌剂混合物的疗效.
确定了创造强大,可持续的杀真菌剂组合的潜力.
该研究为改善疾病管理和减少环境影响提供了一个框架.

关键词:

斯过程是高斯过程.ML ML 在 ML 杀虫剂混合物杀虫剂混合物阅读横向的阅读.支持矢量机器的支持矢量机器.

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