Drug–target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism
Open accessAbstract
Motivation: Accurately identifying the drug–target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. Results: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug–Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. Availability and implementation: https://github.com/XuLew/MIDTI.
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Various computational models for target prediction have been developed, leading to notable improvements in predictive accuracy and efficiency.
“To this end, various computational models for target prediction have been developed, leading to notable improvements in predictive accuracy and efficiency38–40.”
From Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha (2025) · cites this paper as [39]
The Introduction describes several computational approaches to drug–target prediction, and Results §3.2 reports improved predictive accuracy for the paper’s MIDTI model against baselines. The Introduction says computational approaches aim to accelerate screening, but the passages do not establish a notable improvement in predictive efficiency; that part of the claim is not supported. (LLM, conf=0.82)
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- Authors
- Song, W., Xu, L., Han, C., Tian, Z., & Zou, Q.
- Affiliations
- Zhengzhou University (CN)
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Tian, Zhen
- Zou, Quan
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-06
- Volume
- 40
- Issue
- 6
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11164831
- OpenAlex
- W4399369411
- Cited by
- 47
- References
- 23
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 6
- FWCI
- 9.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation of China (62371423, 61801432)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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, drug repurposing, drug discovery
- MeSH terms
- Algorithms, Pharmaceutical Preparations, Humans, Computational Biology, Drug Discovery, Drug Repositioning
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