Predict the carcinogenicity of compounds with SGCN
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...
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2022-06-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000150248 |
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| _version_ | 1849420458018996224 |
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| author | Wei Ruobing He Jiafeng Qiu Xiaofang Liu Qi |
| author_facet | Wei Ruobing He Jiafeng Qiu Xiaofang Liu Qi |
| author_sort | Wei Ruobing |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-91dbbc063d0a469f990c5bd5f6364b23 |
| institution | Kabale University |
| issn | 0258-7998 |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj-art-91dbbc063d0a469f990c5bd5f6364b232025-08-20T03:31:45ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982022-06-01486333510.16157/j.issn.0258-7998.2120803000150248Predict the carcinogenicity of compounds with SGCNWei Ruobing0He Jiafeng1Qiu Xiaofang2Liu Qi3College of Information Engineering，Guangdong University of Technology，Guangzhou 510006，ChinaCollege of Information Engineering，Guangdong University of Technology，Guangzhou 510006，ChinaCollege of Information Engineering，Guangdong University of Technology，Guangzhou 510006，ChinaCollege of Information Engineering，Guangdong University of Technology，Guangzhou 510006，ChinaThe 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.http://www.chinaaet.com/article/3000150248spatial graph convolutional networkclassification modelcarcinogenicity of compounds |
| spellingShingle | Wei Ruobing He Jiafeng Qiu Xiaofang Liu Qi Predict the carcinogenicity of compounds with SGCN Dianzi Jishu Yingyong spatial graph convolutional network classification model carcinogenicity of compounds |
| title | Predict the carcinogenicity of compounds with SGCN |
| title_full | Predict the carcinogenicity of compounds with SGCN |
| title_fullStr | Predict the carcinogenicity of compounds with SGCN |
| title_full_unstemmed | Predict the carcinogenicity of compounds with SGCN |
| title_short | Predict the carcinogenicity of compounds with SGCN |
| title_sort | predict the carcinogenicity of compounds with sgcn |
| topic | spatial graph convolutional network classification model carcinogenicity of compounds |
| url | http://www.chinaaet.com/article/3000150248 |
| work_keys_str_mv | AT weiruobing predictthecarcinogenicityofcompoundswithsgcn AT hejiafeng predictthecarcinogenicityofcompoundswithsgcn AT qiuxiaofang predictthecarcinogenicityofcompoundswithsgcn AT liuqi predictthecarcinogenicityofcompoundswithsgcn |