HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction

TL;DR

HGTDP-DTA introduces a hybrid Graph-Transformer model with dynamic prompt tuning to improve drug-target binding affinity prediction by effectively integrating structural and contextual information, outperforming existing methods.

Contribution

This paper presents a novel hybrid Graph-Transformer framework with dynamic prompt tuning for enhanced DTA prediction, combining structural and sequence data more effectively than prior models.

Findings

Outperforms state-of-the-art DTA prediction methods on Davis and KIBA datasets.

Demonstrates improved generalization ability in drug-target affinity prediction.

Effectively integrates structural and contextual information through multi-view feature fusion.

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.