Application of Intelligent Support Vector Machines to Predict the Properties/Toxicity of Polycyclic Aromatic Hydrocarbons

Abstract

The invention relates to a method for predicting the properties / toxicity of polycyclic aromatic hydrocarbons by using an intelligent support vector machine. According to the measured molecular structure of polycyclic aromatic hydrocarbons, the method uses quantitative structure-activity relationship technology to establish a polycyclic aromatic hydrocarbon cyclization index prediction model and multiple The carcinogenicity prediction model of cycloaromatic hydrocarbons, using the support vector machine algorithm, realizes the processing of small sample, nonlinear and high-dimensional problems. The grid search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the model, which avoids the influence of parameters and further increases the accuracy of the model. The invention can quickly predict the properties and toxicity of unknown polycyclic aromatic hydrocarbons by using the intelligent optimization support vector machine, improves the test efficiency compared with the traditional toxicological test experiment, and improves the generalization ability compared with the traditional statistical prediction method. Compared with the normal algorithm, the influence of parameters is avoided. It realizes programming and can provide a reference decision-making basis for the environmental assessment of polycyclic aromatic hydrocarbons.

Description

technical field [0001] The present invention relates to a molecular quantitative structure-property / activity correlation research method, especially the application and comparison of an intelligent optimization method, that is, a method for predicting the property / toxicity quantitative structure-activity of polycyclic aromatic hydrocarbons by applying an intelligent optimization support vector machine . Background technique [0002] With the acceleration of the global development process, the degree of air pollution has further intensified. Atmospheric suspended particulate matter is one of the important air pollutants that affect the quality of the air environment and endanger human health. Atmospheric particulate matter is highly correlated with environmental problems such as ozone layer depletion, global warming, and acid rain, and is closely related to severe air pollution such as sandstorms and photochemical smog. Epidemiological surveys and studies have shown that as ...

AI Technical Summary

Problems solved by technology

However, in the application of the support vector machine method, it is only a comparative study with other methods, and the selection of the support vector machine parameters is not well considered, which leads to the introduction of inaccurate parameters and leads to the decline of generalization ability.

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