ArticleACS omega2020
Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed.
- Telmisartan reduces systemic inflammation and alters the renin-angiotensin system in mild COVID-19.Scientific reports · 2025Trial
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Article
- Anti-cancer activity elucidation of geissolosimine as an MDM2-p53 interaction inhibitor: An in-silico study.PloS one · 2025Article
- Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure.Toxicology and applied pharmacology · 2024Article
- In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery.Journal of chemical information and modeling · 2024Article
- SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CLBMC chemistry · 2024Article
- Exploring 7β-amino-6-nitrocholestens as COVID-19 antivirals:RSC medicinal chemistry · 2024Article
- Identification of Phytochemicals from Arabian Peninsula Medicinal Plants as Strong Binders to SARS-CoV-2 Proteases (3CLMolecules (Basel, Switzerland) · 2024Article
- Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of NaPharmacological reports : PR · 2023Article
- Alpha-glucosidase inhibitory activities of astilbin contained in Bauhinia strychnifolia Craib. stems: an investigation by in silico and in vitro studies.BMC complementary medicine and therapies · 2023Article
- Article
- A review of SARS-CoV-2 drug repurposing: databases and machine learning models.Frontiers in pharmacology · 2023Review
- In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning.Molecules (Basel, Switzerland) · 2022Article
- Progress on COVID-19 Chemotherapeutics Discovery and Novel Technology.Molecules (Basel, Switzerland) · 2022Review
- DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.Nature communications · 2022Article
- Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook.The Science of the total environment · 2022Review
- Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2021Review
- Article
- Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.
Identifiers
What Socratic holds
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 Socratic graph.