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.

本研究提出了一种新颖的 DTA 预测方法，名为 HGTDP-DTA，该方法利用混合图转换器框架中的动态提示，生成每个药物 - 靶点对的上下文特定提示，进而增强模型捕捉独特的相互作用，并采用多视图特征融合方法将分子图视图和亲和子图视图投影到一个公共特征空间，从而高效地结合了结构和上下文信息。实验结果表明，HGTDP-DTA 在预测性能和泛化能力方面均优于现有的 DTA 预测方法。