摘要
癌症患者的激增引起了全世界的关注,许多研究者将目光放在了对化合物致癌性的评估上,但这是一项极其具有挑战性的任务。本实验获取了341种实验数据,利用三维图卷积网络(SGCN),建立了对化合物致癌性的预测模型。结果表明:对化合物进行致癌性预测的SGCN分类模型准确率高达96.9%,比其余模型效果更好,这表明SGCN模型能够准确地对化学品进行分类,并且在实际应用中具有相当大的潜力。
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.
作者
魏若冰
何家峰
邱晓芳
刘旗
Wei Ruobing;He Jiafeng;Qiu Xiaofang;Liu Qi(College of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China)
出处
《电子技术应用》
2022年第6期33-35,41,共4页
Application of Electronic Technique
基金
国家自然科学基金(61571140)。
关键词
三维图卷积网络
分类模型
致癌化合物
spatial graph convolutional network
classification model
carcinogenicity of compounds
