ArticleJournal of medicinal chemistry2022
Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling.
Article in Journal of medicinal chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Expanded Tox21 Biological Assay Panel for the Prediction of Drug-Induced Liver Injury and Cardiotoxicity.Environmental health perspectives · 2026Article
- Lead optimization of 3-indoleacetonitrile identifies candidate compounds with improved anti-influenza activity and safety profiles.Frontiers in microbiology · 2026Article
- Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.Communications chemistry · 2025Article
- Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.ACS omega · 2024Article
- Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis.Molecular diversity · 2024Article
- On the origins of SARS-CoV-2 main protease inhibitors.RSC medicinal chemistry · 2024Review
- Review
- Selective, broad-spectrum antiviral photodynamic disinfection with dicationic imidazolyl chlorin photosensitizers.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2023Article
- Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.Journal of chemical information and modeling · 2023Article
- Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates.Journal of chemical information and modeling · 2023Article
- Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.PloS one · 2023Article
- In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning.Molecules (Basel, Switzerland) · 2022Article
- Beyond the vaccines: a glance at the small molecule and peptide-based anti-COVID19 arsenal.Journal of biomedical science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 μM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.
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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.