HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction
Xiao, Xi Wang, Wentao Xie, Jiacheng Zhu, Lijing Chen, Gaofei Li, Zhengji Wang, Tianyang Xu, Min
Xiao, Xi
Wang, Wentao
Xie, Jiacheng
Zhu, Lijing
Chen, Gaofei
Li, Zhengji
Wang, Tianyang
Xu, Min
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Drug target binding affinity (DTA) is a key criterion for drug screening. Existing experimental methods are time-consuming and rely on limited structural and domain information. While learning-based methods can model sequence and structural information, they struggle to integrate contextual data and often lack comprehensive modeling of drug-target interactions. In this study, we propose a novel DTA prediction method, termed HGTDP-DTA, which utilizes dynamic prompts within a hybrid Graph-Transformer framework. Our method generates context-specific prompts for each drug-target pair, enhancing the model’s ability to capture unique interactions. The introduction of prompt tuning further optimizes the prediction process by filtering out irrelevant noise and emphasizing task-relevant information, dynamically adjusting the input features of the molecular graph. The proposed hybrid Graph-Transformer architecture combines structural information from Graph Convolutional Networks (GCNs) with sequence information captured by Transformers, facilitating the interaction between global and local information. Additionally, we adopted the multi-view feature fusion method to project molecular graph views and affinity subgraph views into a common feature space, effectively combining structural and contextual information. Experiments on two widely used public datasets, Davis and KIBA, show that HGTDP-DTA outperforms state-of-the-art DTA prediction methods in both prediction performance and generalization ability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Citation
X. Xiao et al., “HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction,” pp. 340–354, 2025, doi: 10.1007/978-981-96-6585-3_24
Source
Lecture Notes in Computer Science
Conference
31st International Conference on Neural Information Processing, ICONIP 2024
Keywords
Drug-target binding affinity, Graph convolutional network, Prompt, Transformer
Subjects
Source
31st International Conference on Neural Information Processing, ICONIP 2024
Publisher
Springer Nature