Genetic algorithm multiple linear regression and machine learning-driven QSTR modeling for the acute toxicity of sterol biosynthesis inhibitor fungicides

Abstract

Sterol Biosynthesis Inhibitors (SBIs) are a major class of fungicides used globally. Their widespread application in agriculture raises concerns about potential harm and toxicity to non-target organisms, including humans. To address these concerns, a quantitative structure-toxicity relationship (QSTR) modeling approach has been developed to assess the acute toxicity of 45 different SBIs. The genetic algorithm (GA) was used to identify key molecular descriptors influencing toxicity. These descriptors were then used to build robust QSTR models using multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN) algorithms. The Cross-validation, Y-randomization test, applicability domain methods, and external validation were carried out to evaluate the accuracy and validity of the generated models. The MLR model exhibited satisfactory predictive performance, with an R2 of 0.72. The SVR and ANN models obtained R2 values of 0.7 and 0.8, respectively. ANN model demonstrated superior performance compared to other models, achieving R2cv and R2test values of 0.74 and 0.7, respectively. The models passed both internal and external validation, indicating their robustness. These models offer a valuable tool for risk assessment, enabling the evaluation of potential hazards associated with future applications of SBIs.

Keywords

• Ergosterol
• Demethylation inhibitor
• Multi-layer perceptron
• QSAR
• SBIs