Heterogeneous Biological Graph Convolutional Network for Drug-target Interaction Prediction
Haoran Zhu, Jianjia Wang, Zhen Hua, Chaoqun Wang, Zimu Zhang, Tong Yu, Ling Ge
Abstract:
Drug–target interaction prediction plays a critical role in drug discovery by identifying potential therapeutic targets and elucidating underlying molecular mechanisms. However, existing computational methods generally rely on limited biological modalities and inadequately capture heterogeneous associations. To overcome these limitations, we propose a Heterogeneous Biological Graph Convolutional Network (HBGCN) that employs a hierarchical graph propagation architecture to integrate multimodal biological information and learn homogeneous and heterogeneous representations for drug–target interaction prediction.
Introduction:
The prediction of drug–target interactions (DTIs) supports drug repositioning, adverse drug reaction detection, and molecular mechanism elucidation through the systematic analysis of binding patterns between bioactive compounds and targets. Traditional DTI identification primarily relies on in vivo and in vitro experiments, including high-throughput screening and pharmacokinetic evaluation.
Materials and Methods:
To comprehensively capture complex associations, drug-target relationships are further classified into direct and indirect interactions. The HBGCN framework comprises three major components. The drug-target meta-path component leverages relational pathways to enhance biological interpretability. The similarity network integrates multiple similarity measures to refine interaction patterns among entities.
Discussion:
In this section, we present the experimental settings and results. Extensive experiments are conducted to evaluate the performance of HBGCN, including comparisons of predictive accuracy with baseline methods, drug–target case studies, ablation studies, and hyperparameter analyses.
Citation: Zhu H, Wang J, Hua Z, Wang C, Zhang Z, Yu T, et al. (2026) Heterogeneous biological graph convolutional network for drug-target interaction prediction. PLoS One 21(5): e0348895. https://doi.org/10.1371/journal.pone.0348895
Editor: Muhammad Mateen, Soochow University, CHINA
Received: October 15, 2025; Accepted: April 22, 2026; Published: May 19, 2026.
Copyright: © 2026 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files. The minimal dataset and accompanying code supporting the findings of this study are fully available without restriction at https://github.com/Saxon0918/HBGCN.
Funding: This work is supported by the Natural Science Foundation of the Jiangsu Higher Educational Institution of China (Grant No. 24KJB510049), and the Research Development Fund (RDF-23-01-044) at Xi’an Jiaotong-Liverpool University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
