ArticleBMC bioinformatics2020
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network.
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 39 papers.
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Who cites it
39 citing papers in PubMed, 120 citations in OpenAlex.
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A comprehensive review of neural network-based approaches for drug-target interaction prediction.Molecular diversity · 2026Review
- BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks.Quantitative biology (Beijing, China) · 2026Article
- Article
- The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer.NPJ digital medicine · 2025Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research.Bioengineering (Basel, Switzerland) · 2025Review
- DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.BMC bioinformatics · 2025Article
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.BMC biology · 2024Article
- Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams.Antibiotics (Basel, Switzerland) · 2024Review
- Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.Bioinformatics (Oxford, England) · 2024Article
- FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug-target interaction prediction.Bioinformatics (Oxford, England) · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs.Frontiers in pharmacology · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time.
resultsWe propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods.
conclusionsAll the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.
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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 Socratic graph.