# GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU

arXiv: https://arxiv.org/abs/2204.11857 · HTML: https://mlsanity.com/paper/2204.11857/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2003.13902/) (2020)

## Built on by (most cited first, 1 of 1)

- [GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction](https://mlsanity.com/paper/2407.10452/) (2024)

## Abstract (arXiv)

The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on GraphDTA, but we consider that protein sequences are long sequences, so simple CNN cannot capture the context dependencies in protein sequences well. Therefore, we improve it by interpreting the protein sequences as time series and extracting their features using Gate Recurrent Unit(GRU) and Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method is similar to that of GraphDTA, but uses two different graph convolution methods. Subsequently, the representation of drugs and proteins are concatenated for final prediction. We evaluate the proposed model on two benchmark datasets. Our model outperforms some state-of-the-art deep learning methods, and the results demonstrate the feasibility and excellent feature capture ability of our model.