Drug-target affinity prediction method based on consistent expression of heterogeneous data

TL;DR

This paper introduces a deep learning approach combining modified GRU and GNN to predict drug-target binding affinity, aiming to accelerate drug discovery by computational screening.

Contribution

It presents a novel deep learning model that integrates sequence and molecular features for more accurate drug-target affinity prediction.

Findings

Achieves high accuracy on DAVIS and KIBA datasets

Demonstrates effectiveness over traditional methods

Provides a scalable approach for drug discovery

Abstract

The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules. However, traditional experimental methods for discovering potential small drug molecules are labor-intensive and time-consuming. There is currently a lot of interest in building computational models to screen small drug molecules using drug molecule-related databases. In this paper, we propose a method for predicting drug-target binding affinity using deep learning models. This method uses a modified GRU and GNN to extract features from the drug-target protein sequences and the drug molecule map, respectively, to obtain their feature vectors. The combined vectors are used as vector representations of drug-target molecule pairs and then fed into a fully connected network to predict drug-target binding affinity. This proposed model demonstrates its accuracy and effectiveness in predicting drug-target binding affinity on the DAVIS and KIBA datasets.