Abstract：
Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R2 and R2cv at 0.91 and 0.81, respectively. For external validation, the R2test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence.
摘要：
杀菌剂混合物是延缓杀菌剂抗性发展的有效策略。在这项研究中,使用固定比例的射线设计方法来生成50种具有不同作用方式的五种杀菌剂的二元混合物。然后使用CA和IA模型分析这些混合物的相互作用。进行QSAR建模以通过多元线性回归（MLR）评估其杀菌活性，支持向量机(SVM)，和人工神经网络(ANN)。大多数混合物表现出添加剂相互作用，CA模型在预测杀菌活性方面比IA模型更准确。MLR模型在通过遗传算法选择的理论描述符与杀菌活性之间显示出良好的线性相关性。然而,两种基于ML的模型都表现出比MLR模型更好的预测性能。人工神经网络模型显示出比SVM模型略好的可预测性，R2和R2cv分别为0.91和0.81。对于外部验证，R2试验值为0.845。相比之下,对于相同的指标,SVM模型的值分别为0.91,0.78和0.77.总之,提出的基于ML的模型可以是开发有效的杀真菌混合物以延迟杀真菌抗性出现的有价值的工具。
