A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction

TL;DR

This paper introduces Graph-in-Graph, a novel GNN-based framework that effectively combines molecular and network features for improved drug-target interaction prediction, outperforming existing methods.

Contribution

The paper presents a new graph-in-graph framework that integrates transductive and inductive learning for detailed drug-target interaction modeling.

Findings

GiG significantly outperforms existing approaches

Effective integration of molecular and network features

New benchmark dataset for DTI prediction

Abstract

Accurately predicting drug-target interactions (DTIs) is pivotal for advancing drug discovery and target validation techniques. While machine learning approaches including those that are based on Graph Neural Networks (GNN) have achieved notable success in DTI prediction, many of them have difficulties in effectively integrating the diverse features of drugs, targets and their interactions. To address this limitation, we introduce a novel framework to take advantage of the power of both transductive learning and inductive learning so that features at molecular level and drug-target interaction network level can be exploited. Within this framework is a GNN-based model called Graph-in-Graph (GiG) that represents graphs of drug and target molecular structures as meta-nodes in a drug-target interaction graph, enabling a detailed exploration of their intricate relationships. To evaluate the proposed model, we have compiled a special benchmark comprising drug SMILES, protein sequences, and their interaction data, which is interesting in its own right. Our experimental results demonstrate that the GiG model significantly outperforms existing approaches across all evaluation metrics, highlighting the benefits of integrating different learning paradigms and interaction data.