ArticleProceedings of the National Academy of Sciences of the United States of America2021
Deep learning identifies synergistic drug combinations for treating COVID-19.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.
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Who cites it
75 citing papers in PubMed, 1 synthesis or guideline pooled it, 169 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Chemistry and Biological Activity of 11Molecules (Basel, Switzerland) · 2026Review
- DeepSynBa: actionable drug combination prediction with complete dose-response profiles.Bioinformatics (Oxford, England) · 2026Article
- DualKG-DC: A Drug-Centric Dual-Layer Knowledge Graph Framework for Drug Combination Prediction.Journal of medical systems · 2026Article
- Review
- Role of Artificial Intelligence in Infectious Diseases and Antimicrobial Resistance: A Comprehensive Review on Diagnostic, Treatment, and Prevention Aspects.Saudi medical journal · 2026Review
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Model-informed prediction of antiviral drug combination synergy from sparse data using complementary mechanistic and pharmacodynamic approaches.Frontiers in pharmacology · 2026Article
- Environment-by-Environment Interactions Differ Across Genetic Backgrounds.Molecular biology and evolution · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Computational drug repurposing: approaches, evaluation of in silico resources and case studies.Nature reviews. Drug discovery · 2025Review
- MADSP: predicting anti-cancer drug synergy through multi-source integration and attention-based representation learning.Bioinformatics (Oxford, England) · 2025Article
- AI-driven discovery of synergistic drug combinations against pancreatic cancer.Nature communications · 2025Article
- An explainable deep learning platform for molecular discovery.Nature protocols · 2025Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single-agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.
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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.