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

arXiv: https://arxiv.org/abs/2412.19589 · HTML: https://mlsanity.com/paper/2412.19589/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [GEFA: Early Fusion Approach in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2009.12146/) (2020)
- [Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network](https://mlsanity.com/paper/1805.10988/) (2018)

## Built on by (most cited first, 3 of 3)

- [Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction](https://mlsanity.com/paper/2503.17666/) (2025)
- [Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning](https://mlsanity.com/paper/2506.00936/) (2025)
- [HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction](https://mlsanity.com/paper/2510.27281/) (2025)

## Abstract (arXiv)

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.