ArticleBMC bioinformatics2020
Drug-target interaction prediction using semi-bipartite graph model and deep learning.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 60 citations in OpenAlex.
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Relational similarity-based graph contrastive learning for DTI prediction.Briefings in bioinformatics · 2025Article
- Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization.PloS one · 2025Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining.BMC bioinformatics · 2023Article
- A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.PLoS computational biology · 2023Article
- Graph embedding and geometric deep learning relevance to network biology and structural chemistry.Frontiers in artificial intelligence · 2023Review
- A novel method for drug-target interaction prediction based on graph transformers model.BMC bioinformatics · 2022Article
- GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery.BMC bioinformatics · 2022Article
- HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.BMC bioinformatics · 2022Article
- Biclique extension as an effective approach to identify missing links in metabolic compound-protein interaction networks.Bioinformatics advances · 2022Article
- CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.Frontiers in molecular biosciences · 2022Article
- PPA-GCN: A Efficient GCN Framework for Prokaryotic Pathways Assignment.Frontiers in genetics · 2022Article
- DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding.BMC bioinformatics · 2021Article
- Topological network measures for drug repositioning.Briefings in bioinformatics · 2021Article
- A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning.Cancers · 2021Article
- Link Prediction in Multipartite Graphs With Application to Drug Repositioning Studies.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted features, similarity metrics and machine learning methods have been proposed for predicting drug-target interactions. However, these methods cannot fully learn the underlying relations between drugs and targets. In this paper, we propose anew framework for drug-target interaction prediction that learns latent features from drug-target interaction network.
resultsWe present a framework to utilize the network topology and identify interacting and non-interacting drug-target pairs. We model the problem as a semi-bipartite graph in which we are able to use drug-drug and protein-protein similarity in a drug-protein network. We have then used a graph labeling method for vertex ordering in our graph embedding process. Finally, we employed deep neural network to learn the complex pattern of interacting pairs from embedded graphs. We show our approach is able to learn sophisticated drug-target topological features and outperforms other state-of-the-art approaches.
conclusionsThe proposed learning model on semi-bipartite graph model, can integrate drug-drug and protein-protein similarities which are semantically different than drug-protein information in a drug-target interaction network. We show our model can determine interaction likelihood for each drug-target pair and outperform other heuristics.
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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.