ArticleMolecular informatics2024
A community effort in SARS-CoV-2 drug discovery.
Article in Molecular informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Inhibitors of GapN-dependent NADPH supply as potential lead compounds for novel therapeutics againstVirulence · 2026Article
- Molecular Glues Stabilize Water-Mediated Hydrogen Bonds in Ternary Complexes.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- An Investigational Study on the Role of ADME Agents' Genetic Variation on DD217 Pharmacokinetics and Safety Profile.Pharmaceuticals (Basel, Switzerland) · 2025Article
- GENEOnet: a breakthrough in protein binding pocket detection using group equivariant non-expansive operators.Scientific reports · 2025Article
- A New Fragment-Based Pharmacophore Virtual Screening Workflow Identifies Potent Inhibitors of SARS-CoV-2 NSP13 Helicase.Journal of computational chemistry · 2025Article
- A Database for Large-Scale Docking and Experimental Results.Journal of chemical information and modeling · 2025Article
- MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.Journal of chemical information and modeling · 2025Article
- A Reflection on the Use of Molecular Simulation to Respond to SARS-CoV-2 Pandemic Threats.The journal of physical chemistry letters · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
152 authors.
Funding
Abstract
The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.
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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.