Drug-target binding affinity prediction based on power graph and word2vec
Abstract Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, m...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12920-024-02073-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594352297213952 |
|---|---|
| author | Jing Hu Shuo Hu Minghao Xia Kangxing Zheng Xiaolong Zhang |
| author_facet | Jing Hu Shuo Hu Minghao Xia Kangxing Zheng Xiaolong Zhang |
| author_sort | Jing Hu |
| collection | DOAJ |
| description | Abstract Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions. Results Compared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed. Conclusions In this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance. |
| format | Article |
| id | doaj-art-8f2176618a6e45818397312693bd05f1 |
| institution | Kabale University |
| issn | 1755-8794 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj-art-8f2176618a6e45818397312693bd05f12025-01-19T12:42:34ZengBMCBMC Medical Genomics1755-87942025-01-0118S111110.1186/s12920-024-02073-5Drug-target binding affinity prediction based on power graph and word2vecJing Hu0Shuo Hu1Minghao Xia2Kangxing Zheng3Xiaolong Zhang4School of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologyAbstract Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions. Results Compared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed. Conclusions In this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.https://doi.org/10.1186/s12920-024-02073-5Drug-target affinityPower graphWord2vecGraph neural networkDrug retargeting |
| spellingShingle | Jing Hu Shuo Hu Minghao Xia Kangxing Zheng Xiaolong Zhang Drug-target binding affinity prediction based on power graph and word2vec BMC Medical Genomics Drug-target affinity Power graph Word2vec Graph neural network Drug retargeting |
| title | Drug-target binding affinity prediction based on power graph and word2vec |
| title_full | Drug-target binding affinity prediction based on power graph and word2vec |
| title_fullStr | Drug-target binding affinity prediction based on power graph and word2vec |
| title_full_unstemmed | Drug-target binding affinity prediction based on power graph and word2vec |
| title_short | Drug-target binding affinity prediction based on power graph and word2vec |
| title_sort | drug target binding affinity prediction based on power graph and word2vec |
| topic | Drug-target affinity Power graph Word2vec Graph neural network Drug retargeting |
| url | https://doi.org/10.1186/s12920-024-02073-5 |
| work_keys_str_mv | AT jinghu drugtargetbindingaffinitypredictionbasedonpowergraphandword2vec AT shuohu drugtargetbindingaffinitypredictionbasedonpowergraphandword2vec AT minghaoxia drugtargetbindingaffinitypredictionbasedonpowergraphandword2vec AT kangxingzheng drugtargetbindingaffinitypredictionbasedonpowergraphandword2vec AT xiaolongzhang drugtargetbindingaffinitypredictionbasedonpowergraphandword2vec |