HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction
<|reference_start|>HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction: 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.<|reference_end|>
@article{xiao2024hgtdp-dta:,
    title={HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target
  Binding Affinity Prediction},
    author={Xi Xiao and Wentao Wang and Jiacheng Xie and Lijing Zhu and Gaofei
  Chen and Zhengji Li and Tianyang Wang and Min Xu},
    journal={arXiv preprint arXiv:2406.17697},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17697},
    primaryClass={cs.LG cs.AI cs.CV}
}