ArticleACS pharmacology & translational science2021
Hybrid
Article in ACS pharmacology & translational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- A Hybrid Experimental and in silico Platform for ITPK1 Chemical Probe Discovery.SLAS discovery : advancing life sciences R & D · 2026Article
- Drugs against broad-spectrum of coronaviruses.Frontiers in immunology · 2026Review
- Integrated Approach of Machine Learning and High-Throughput Screening to Identify Chemical Probe Candidates Targeting Aldehyde Dehydrogenases.ACS pharmacology & translational science · 2025Article
- Discovery of SARS-CoV-2 Nsp14-Methyltransferase (MTase) Inhibitors by Harnessing Scaffold-Centric Exploration of the Ultra Large Chemical Space.ACS pharmacology & translational science · 2025Article
- AI-driven discovery of synergistic drug combinations against pancreatic cancer.Nature communications · 2025Article
- Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.Journal of chemical information and modeling · 2025Article
- One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening.Journal of cheminformatics · 2025Article
- Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking.International journal of molecular sciences · 2024Article
- Machine learning and protein allostery.Trends in biochemical sciences · 2023Review
- Chronic Lung and Respiratory Conditions Affecting Lungs and Airways.ACS pharmacology & translational science · 2022Article
- Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents.ACS pharmacology & translational science · 2022Article
- Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.bioRxiv : the preprint server for biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC
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.