Abstract: This paper uses Quantitative Structure-Property Relationship correlation method to predict the Lower Explosive Limit experimental values of 458 organic compounds. First of all, Dragon 2.1 software was used to calculate and pre-screened 708 molecular descriptors. Then Genetic Algorithm was used to determine 5 characteristic molecular descriptors as the input variables of the model. Finally, SPSS software was used to construct the Multiple Linear Regression linear model, and MATLAB software was used to construct Support Vector Machine and Artificial Neural Network nonlinear models. The results showed that for MLR model, the complex correlation coefficient R2 of training and test set were 0.838 7 and 0.858 8 respectively. For SVM model, R2 is 0.856 9 and 0.877 9. For ANN model, R2 is 0.928 4 and 0.932 8. It showed that the prediction effect of SVM model was better than MLR model, and the prediction effect of ANN model was better than SVM model. There was a strong nonlinear relationship between the LEL of organic compounds and their molecular structure. In addition, the performance of the ANN model was evaluated by internal and external verification methods and compared with previous studies, which proved that the ANN model had the best prediction accuracy for the LEL. The Wiliams diagram was drawn to analyze the application domain of the model, and the model had well generalization ability and robustness. The LEL of organic compounds was predicted by the method of QSPR, which provided effective theoretical research and technical support for the risk control and safety process research of hazardous chemicals.

Key words: Quantitative Structure-Property Relationship, Artificial Neural Network, Multiple Linear Regression, Support Vector Machine, Lower Explosive Limit, organic compound