Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations

TL;DR

This paper introduces MTINet+, a graph neural network that combines biomedical network topology and molecule structural features to improve molecule-target interaction prediction, addressing limitations of existing methods.

Contribution

The paper presents a novel pseudo-siamese GNN model that integrates topological and structural data for more accurate and robust interaction prediction.

Findings

MTINet+ outperforms state-of-the-art baselines in various tasks.

The model is robust against sparse biomedical networks.

Combines network topology with molecule structure for better predictions.

Abstract

The study of molecule-target interaction is quite important for drug discovery in terms of target identification, hit identification, pathway study, drug-drug interaction, etc. Most existing methodologies utilize either biomedical network information or molecule structural features to predict potential interaction link. However, the biomedical network information based methods usually suffer from cold start problem, while structure based methods often give limited performance due to the structure/interaction assumption and data quality. To address these issues, we propose a pseudo-siamese Graph Neural Network method, namely MTINet+, which learns both biomedical network topological and molecule structural/chemical information as representations to predict potential interaction of given molecule and target pair. In MTINet+, 1-hop subgraphs of given molecule and target pair are extracted from known interaction of biomedical network as topological information, meanwhile the molecule structural and chemical attributes are processed as molecule information. MTINet+ learns these two types of information as embedding features for predicting the pair link. In the experiments of different molecule-target interaction tasks, MTINet+ significantly outperforms over the state-of-the-art baselines. In addition, in our designed network sparsity experiments , MTINet+ shows strong robustness against different sparse biomedical networks.