SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
The prediction of drug–target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug–target affinity) was implemented. Unlike previous graph-based methods, the proposed model utilized self-attentio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/16/8993 |
| _version_ | 1797523524409098240 |
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| author | Shugang Zhang Mingjian Jiang Shuang Wang Xiaofeng Wang Zhiqiang Wei Zhen Li |
| author_facet | Shugang Zhang Mingjian Jiang Shuang Wang Xiaofeng Wang Zhiqiang Wei Zhen Li |
| author_sort | Shugang Zhang |
| collection | DOAJ |
| description | The prediction of drug–target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug–target affinity) was implemented. Unlike previous graph-based methods, the proposed model utilized self-attention mechanisms on the drug molecular graph to obtain effective representations of drugs for DTA prediction. Features of each atom node in the molecular graph were weighted using an attention score before being aggregated as molecule representation. Various self-attention scoring methods were compared in this study. In addition, two pooing architectures, namely, global and hierarchical architectures, were presented and evaluated on benchmark datasets. Results of comparative experiments on both regression and binary classification tasks showed that SAG-DTA was superior to previous sequence-based or other graph-based methods and exhibited good generalization ability. |
| first_indexed | 2024-03-10T08:44:14Z |
| format | Article |
| id | doaj.art-bbe48946b63e44818b58e6ef0d341247 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T08:44:14Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-bbe48946b63e44818b58e6ef0d3412472023-11-22T08:03:44ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-08-012216899310.3390/ijms22168993SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph NetworkShugang Zhang0Mingjian Jiang1Shuang Wang2Xiaofeng Wang3Zhiqiang Wei4Zhen Li5College of Computer Science and Technology, Ocean University of China, Qingdao 266100, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaMindRank AI Ltd., Hangzhou 311113, ChinaCollege of Computer Science and Technology, Ocean University of China, Qingdao 266100, ChinaCollege of Computer Science and Technology, Qingdao University, Qingdao 266071, ChinaThe prediction of drug–target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug–target affinity) was implemented. Unlike previous graph-based methods, the proposed model utilized self-attention mechanisms on the drug molecular graph to obtain effective representations of drugs for DTA prediction. Features of each atom node in the molecular graph were weighted using an attention score before being aggregated as molecule representation. Various self-attention scoring methods were compared in this study. In addition, two pooing architectures, namely, global and hierarchical architectures, were presented and evaluated on benchmark datasets. Results of comparative experiments on both regression and binary classification tasks showed that SAG-DTA was superior to previous sequence-based or other graph-based methods and exhibited good generalization ability.https://www.mdpi.com/1422-0067/22/16/8993drug–target affinitygraph neural networkself-attention |
| spellingShingle | Shugang Zhang Mingjian Jiang Shuang Wang Xiaofeng Wang Zhiqiang Wei Zhen Li SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network International Journal of Molecular Sciences drug–target affinity graph neural network self-attention |
| title | SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network |
| title_full | SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network |
| title_fullStr | SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network |
| title_full_unstemmed | SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network |
| title_short | SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network |
| title_sort | sag dta prediction of drug target affinity using self attention graph network |
| topic | drug–target affinity graph neural network self-attention |
| url | https://www.mdpi.com/1422-0067/22/16/8993 |
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