MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction
Abstract Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to...
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BMC
2024-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05984-3 |
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| author | Jinghong Sun Han Wang Jia Mi Jing Wan Jingyang Gao |
| author_facet | Jinghong Sun Han Wang Jia Mi Jing Wan Jingyang Gao |
| author_sort | Jinghong Sun |
| collection | DOAJ |
| description | Abstract Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to machine learning-based prediction. This transition enables a more expedient exploration of potential interactions between drugs and targets, leading to substantial savings in time and funding resources. However, existing methods still face several challenges, such as drug information loss, lack of calculation of the contribution of each modality, and lack of simulation regarding the drug–target binding mechanisms. Results We propose MTAF–DTA, a method for drug–target binding affinity prediction to solve the above problems. The drug representation module extracts three modalities of features from drugs and uses an attention mechanism to update their respective contribution weights. Additionally, we design a Spiral-Attention Block (SAB) as drug–target feature fusion module based on multi-type attention mechanisms, facilitating a triple fusion process between them. The SAB, to some extent, simulates the interactions between drugs and targets, thereby enabling outstanding performance in the DTA task. Our regression task on the Davis and KIBA datasets demonstrates the predictive capability of MTAF–DTA, with CI and MSE metrics showing respective improvements of 1.1% and 9.2% over the state-of-the-art (SOTA) method in the novel target settings. Furthermore, downstream tasks further validate MTAF–DTA’s superiority in DTA prediction. Conclusions Experimental results and case study demonstrate the superior performance of our approach in DTA prediction tasks, showing its potential in practical applications such as drug discovery and disease treatment. |
| format | Article |
| id | doaj-art-a6082d8aefad4a1c84a09e1709b59d3b |
| institution | OA Journals |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | BMC Bioinformatics |
| spelling | doaj-art-a6082d8aefad4a1c84a09e1709b59d3b2025-08-20T02:20:47ZengBMCBMC Bioinformatics1471-21052024-12-0125112110.1186/s12859-024-05984-3MTAF–DTA: multi-type attention fusion network for drug–target affinity predictionJinghong Sun0Han Wang1Jia Mi2Jing Wan3Jingyang Gao4College of Information Science and Technology, Beijing University of Chemical TechnologyCollege of Information Science and Technology, Beijing University of Chemical TechnologyCollege of Information Science and Technology, Beijing University of Chemical TechnologyCollege of Information Science and Technology, Beijing University of Chemical TechnologyCollege of Information Science and Technology, Beijing University of Chemical TechnologyAbstract Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to machine learning-based prediction. This transition enables a more expedient exploration of potential interactions between drugs and targets, leading to substantial savings in time and funding resources. However, existing methods still face several challenges, such as drug information loss, lack of calculation of the contribution of each modality, and lack of simulation regarding the drug–target binding mechanisms. Results We propose MTAF–DTA, a method for drug–target binding affinity prediction to solve the above problems. The drug representation module extracts three modalities of features from drugs and uses an attention mechanism to update their respective contribution weights. Additionally, we design a Spiral-Attention Block (SAB) as drug–target feature fusion module based on multi-type attention mechanisms, facilitating a triple fusion process between them. The SAB, to some extent, simulates the interactions between drugs and targets, thereby enabling outstanding performance in the DTA task. Our regression task on the Davis and KIBA datasets demonstrates the predictive capability of MTAF–DTA, with CI and MSE metrics showing respective improvements of 1.1% and 9.2% over the state-of-the-art (SOTA) method in the novel target settings. Furthermore, downstream tasks further validate MTAF–DTA’s superiority in DTA prediction. Conclusions Experimental results and case study demonstrate the superior performance of our approach in DTA prediction tasks, showing its potential in practical applications such as drug discovery and disease treatment.https://doi.org/10.1186/s12859-024-05984-3Drug–target binding affinityMulti-modal featuresAttention mechanismsNested fusion networks |
| spellingShingle | Jinghong Sun Han Wang Jia Mi Jing Wan Jingyang Gao MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction BMC Bioinformatics Drug–target binding affinity Multi-modal features Attention mechanisms Nested fusion networks |
| title | MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction |
| title_full | MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction |
| title_fullStr | MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction |
| title_full_unstemmed | MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction |
| title_short | MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction |
| title_sort | mtaf dta multi type attention fusion network for drug target affinity prediction |
| topic | Drug–target binding affinity Multi-modal features Attention mechanisms Nested fusion networks |
| url | https://doi.org/10.1186/s12859-024-05984-3 |
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