ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion

TL;DR

ViDTA introduces virtual nodes in GNNs and an attention-based fusion method to improve drug-target affinity prediction by capturing both local and global features more effectively.

Contribution

The paper presents a novel GNN architecture with virtual nodes and an attention-based fusion for enhanced DTA prediction, addressing limitations of previous methods.

Findings

Outperforms state-of-the-art on Davis, Metz, and KIBA benchmarks.

Effectively integrates local and global drug features.

Improves message exchange efficiency in GNNs.

Abstract

Drug-target interaction is fundamental in understanding how drugs affect biological systems, and accurately predicting drug-target affinity (DTA) is vital for drug discovery. Recently, deep learning methods have emerged as a significant approach for estimating the binding strength between drugs and target proteins. However, existing methods simply utilize the drug's local information from molecular topology rather than global information. Additionally, the features of drugs and proteins are usually fused with a simple concatenation operation, limiting their effectiveness. To address these challenges, we proposed ViDTA, an enhanced DTA prediction framework. We introduce virtual nodes into the Graph Neural Network (GNN)-based drug feature extraction network, which acts as a global memory to exchange messages more efficiently. By incorporating virtual graph nodes, we seamlessly integrate local and global features of drug molecular structures, expanding the GNN's receptive field. Additionally, we propose an attention-based linear feature fusion network for better capturing the interaction information between drugs and proteins. Experimental results evaluated on various benchmarks including Davis, Metz, and KIBA demonstrate that our proposed ViDTA outperforms the state-of-the-art baselines.