Predict the carcinogenicity of compounds with SGCN - Wei Ruobing; He Jiafeng; Qiu Xiaofang; Liu Qi (Dianzi Jishu Yingyong)
The rapid increase of the number of cancer patients has attracted worldwide attention. Researchers are very concerned about the assessment of the carcinogenicity of compounds, but this is extremely challenging. In this paper, 341 kinds of experimental data were obtained, and the spatial atom feature combined with the spatial graph convolutional network(SGCN) was used to establish a model that could predict the carcinogenicity of compounds. The results showed that when compared to other models, the classification model of the SGCN was more suited to predicting the carcinogenicity of compounds and had an overall classification accuracy of 96.9%, which showed that the SGCN model could accurately classify chemicals and had considerable potential in practical applications.