ArticleIEEE access : practical innovations, open solutions2020
DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Organoids, organ-on-a-chip, and microtumors: Biomimetic 3D tumor models advancing drug development and precision medicine.Acta pharmaceutica Sinica. B · 2026Review
- NTMFF-DTA: Prediction of Drug-Target Affinity Based on Network Topology and Multi-feature Fusion.Interdisciplinary sciences, computational life sciences · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks.Heliyon · 2025Article
- Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.ACS omega · 2025Article
- Exploring the Therapeutic Potential ofMolecules (Basel, Switzerland) · 2024Article
- Drug-target binding affinity prediction using message passing neural network and self supervised learning.BMC genomics · 2023Article
- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding.BMC bioinformatics · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing.Briefings in bioinformatics · 2023Article
- Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.Multimedia tools and applications · 2023Article
- BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach.PLoS computational biology · 2023Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- Deep learning application detecting SARS-CoV-2 key enzymes inhibitors.Cluster computing · 2023Article
- AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.Briefings in bioinformatics · 2022Article
- Drug Properties Prediction Based on Deep Learning.Pharmaceutics · 2022Article
- Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery.Journal of infection and public health · 2022Review
- An Overview of Organs-on-Chips Based on Deep Learning.Research (Washington, D.C.) · 2022Review
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5 authors.
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Abstract
The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.
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