ArticleThe journal of physical chemistry letters2020
Repositioning of 8565 Existing Drugs for COVID-19.
Article in The journal of physical chemistry letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Recent Advances in the Design of Inhibitors Targeting the Viral Entry and Replication of the SARS-CoV-2 Virus, Driven by In Silico Approaches.Molecules (Basel, Switzerland) · 2026Review
- A Novel Covalent Inhibitor Fragment for the SARS-CoV-2 Main Protease Identified by Target-Specific Deep Learning.ACS chemical biology · 2026Article
- TropMol-Caipora: A Cloud-Based Web Tool to Predict Cruzain Inhibitors by Machine Learning.ACS omega · 2026Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Machine learning analysis of ARVC informed by sodium channel protein-based interactome networks.Frontiers in pharmacology · 2025Article
- Rapid Screening to Identify Antivirals against Persistent and Acute Coxsackievirus B3 Infection.ACS infectious diseases · 2024Article
- Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.Royal Society open science · 2024Article
- Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.Molecular diversity · 2024Article
- Prediction of protein-ligand binding affinity via deep learning models.Briefings in bioinformatics · 2024Review
- Potential Drugs in COVID-19 Management.Current medicinal chemistry · 2024Review
- Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings.Journal of enzyme inhibition and medicinal chemistry · 2023Article
- Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease.Cellular and molecular life sciences : CMLS · 2023Article
- Machine-learning repurposing of DrugBank compounds for opioid use disorder.Computers in biology and medicine · 2023Article
- Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models.Computers in biology and medicine · 2023Article
- A Clinical Insight on New Discovered Molecules and Repurposed Drugs for the Treatment of COVID-19.Vaccines · 2023Review
- A review of SARS-CoV-2 drug repurposing: databases and machine learning models.Frontiers in pharmacology · 2023Review
- Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.Chemical physics · 2023Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.Chemical reviews · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 7.1 million people and led to over 0.4 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than 10 years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, we develop validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 3CL protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.
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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.