HampDTI: a heterogeneous graph automatic meta-path learning method for drug-target interaction prediction

by   Hongzhun Wang, et al.

Motivation: Identifying drug-target interactions (DTIs) is a key step in drug repositioning. In recent years, the accumulation of a large number of genomics and pharmacology data has formed mass drug and target related heterogeneous networks (HNs), which provides new opportunities of developing HN-based computational models to accurately predict DTIs. The HN implies lots of useful information about DTIs but also contains irrelevant data, and how to make the best of heterogeneous networks remains a challenge. Results: In this paper, we propose a heterogeneous graph automatic meta-path learning based DTI prediction method (HampDTI). HampDTI automatically learns the important meta-paths between drugs and targets from the HN, and generates meta-path graphs. For each meta-path graph, the features learned from drug molecule graphs and target protein sequences serve as the node attributes, and then a node-type specific graph convolutional network (NSGCN) which efficiently considers node type information (drugs or targets) is designed to learn embeddings of drugs and targets. Finally, the embeddings from multiple meta-path graphs are combined to predict novel DTIs. The experiments on benchmark datasets show that our proposed HampDTI achieves superior performance compared with state-of-the-art DTI prediction methods. More importantly, HampDTI identifies the important meta-paths for DTI prediction, which could explain how drugs connect with targets in HNs.


page 1

page 2

page 3

page 4


Meta-Path-based Probabilistic Soft Logic for Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction, which aims at predicting wheth...

Heterogeneous Graph Tree Networks

Heterogeneous graph neural networks (HGNNs) have attracted increasing re...

Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

Heterogeneous graph neural networks aim to discover discriminative node ...

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks

Heterogeneous Information Network (HIN) is essential to study complicate...

Interpretable bilinear attention network with domain adaptation improves drug-target prediction

Predicting drug-target interaction is key for drug discovery. Recent dee...

Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions

Predicting the bioactivity of a ligand is one of the hardest and most im...

Interpretable and Efficient Heterogeneous Graph Convolutional Network

Graph Convolutional Network (GCN) has achieved extraordinary success in ...

Please sign up or login with your details

Forgot password? Click here to reset