Cited by 383Computational Drug Discovery MethodsBioinformatics and Genomic NetworksMachine Learning in Bioinformatics

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NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions

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Wan, Fangping, Hong, Lixiang, Xiao, An, Jiang, Tao, Zeng, Jianyang·2019·Bioinformatics

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Abstract

Motivation: Accurately predicting drug–target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. Results: Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g. compound–protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementation: The source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI. Supplementary information: Supplementary data are available at Bioinformatics online.

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Authors: Wan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J.
Affiliations: Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
Tsinghua University (CN)
University of California, Riverside (US)
Corresponding author: Zeng, Jianyang
ORCID: Hong, Lixiang
Xiao, An
Jiang, Tao
Zeng, Jianyang
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2018-07-02
Volume: 35
Issue: 1
Pages: 104-111
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/bty543
OpenAlex: W2952522777
Cited by: 381
References: 38
Citations by year: 2026: 33 · 2025: 59 · 2024: 71 · 2023: 57 · 2022: 59 · 2021: 54 · 2020: 31 · 2019: 16 · 2018: 1
FWCI: 19.45
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Bioinformatics (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (61472205, 81630103)
China’s Youth 1000-Talent Program
Beijing Advanced Innovation Center for Structural Biology
US National Science Foundation (IIS-1646333)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords: drug-target interaction prediction, heterogeneous networks, graph neural networks, drug discovery, drug repurposing
MeSH terms: Drug Development, Computer Simulation, Protein Binding, Software, Drug Discovery, Drug Repositioning

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NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions

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