ReviewBriefings in bioinformatics2022
An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- AMCF-RDP: a self-attention-based multi-source and cascade framework for the identification of drug-protein relationships.Molecular diversity · 2026Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Review
- PyMulSim: a method for computing node similarities between multilayer networks via graph isomorphism networks.BMC bioinformatics · 2024Article
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- MulinforCPI: enhancing precision of compound-protein interaction prediction through novel perspectives on multi-level information integration.Briefings in bioinformatics · 2023Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- Identifying Candidate Gene-Disease Associations via Graph Neural Networks.Entropy (Basel, Switzerland) · 2023Article
- Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence.Journal of the American Chemical Society · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.