杀菌剂二元混合物的联合相互作用:实验研究和机器学习驱动的QSAR建模
Mohsen Abbod1, Ahmad Mohammad2
1Department of Plant Protection, Faculty of Agriculture, Al-Baath University, Homs, Syria. abbod.mohsen111@gmail.com.
Scientific reports
|June 3, 2024
概括
杀菌剂混合物有效地延迟了耐药性. 这项研究开发了预测模型,发现人工神经网络 (ANN) 在设计强效杀菌组合以对抗耐药性方面优越.
科学领域:
- 农业科学 农业科学
- 计算化学计算化学
- 植物病理学 植物病理学
背景情况:
- 抗真菌杀菌剂耐药性是农作物生产的主要威胁.
- 混合策略对于控制真菌杀菌剂耐药性至关重要.
- 预测建模可以优化有效杀菌剂混合物的开发.
研究的目的:
- 评估二元真菌杀菌剂混合物的杀菌活性和相互作用.
- 开发和比较量化结构-活性关系 (QSAR) 模型,以预测杀菌效果.
- 确定最佳的杀菌剂组合,以延迟杀菌剂耐药性.
主要方法:
- 使用固定比率射线设计生成了50种二元真菌杀菌剂混合物.
- 使用组合分析 (CA) 和干扰分析 (IA) 模型分析混合物相互作用.
- 使用多重线性回归 (MLR),支持向量机 (SVM) 和人工神经网络 (ANN) 来进行QSAR建模.
主要成果:
- 大多数杀真菌剂混合物都表现出添加物相互作用.
- 在预测杀菌活性方面,CA模型比IA模型更准确.
- 基于机器学习 (ML) 的模型 (ANN和SVM) 在预测性能方面表现优于MLR.
- 与SVM (R2=0.91,R2cv=0.78,R2test=0.77) 相比,ANN模型显示出更高的可预测性 (R2=0.91,R2cv=0.81,R2test=0.845),而SVM则显示出更高的可预测性.
结论:
- 基于ML的QSAR模型是设计有效的杀菌剂混合物的宝贵工具.
- 开发的ANN模型显示了预测杀菌活性和指导耐药性管理策略的巨大潜力.
- 优化杀真菌剂组合是可持续农业和缓解耐药性发展的关键.
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