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NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
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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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“Producing atom-centered representations based on a weighted combination of their neighbors’ features (i.e., using an attention mechanism) is able to model interactions among reactants for predicting organic reaction outcomes [167].”
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Metadata
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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