ArticleApplied soft computing2021
A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.Computational biology and chemistry · 2022Pooled it
- Standardizing antiviral response metrics for mono- and combination therapies in acute, chronic and latent viral infections.Virology journal · 2026Review
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.Communications chemistry · 2025Article
- GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.Briefings in bioinformatics · 2025Article
- Nature's cryptographic codebreakerFrontiers in microbiology · 2025Article
- Article
- AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.International journal of molecular sciences · 2024Article
- A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.Communications biology · 2024Article
- A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.Engineering in life sciences · 2024Article
- Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network.Current medicinal chemistry · 2024Article
- DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response.Bioinformatics (Oxford, England) · 2023Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- Article
- A new integrated framework for the identification of potential virus-drug associations.Frontiers in microbiology · 2023Article
- A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus-drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.
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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.