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

TL;DR

GDGRU-DTA is a novel deep learning model that predicts drug-target binding affinity by combining graph neural networks for drugs with GRU-based sequence modeling for proteins, outperforming existing methods on benchmark datasets.

Contribution

The paper introduces GDGRU-DTA, integrating GNNs and GRUs to better capture features of drugs and proteins for affinity prediction, addressing limitations of previous CNN-based approaches.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively captures long-range dependencies in protein sequences

Demonstrates high prediction accuracy and feature extraction capability

Abstract

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.