  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.
