Evidence map›Paper›PMID 39613993›Full record

ArticleMolecular diversity2025

Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 M

Annesha Chakraborty, Vignesh Krishnan, Subbiah Thamotharan

Abstract read
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In one paragraph

Article in Molecular diversity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Annesha ChakrabortyBiomolecular Crystallography Laboratory and DBT-Bioinformatics Center, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, 613 401, India.
Vignesh KrishnanBiomolecular Crystallography Laboratory and DBT-Bioinformatics Center, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, 613 401, India.
Subbiah ThamotharanBiomolecular Crystallography Laboratory and DBT-Bioinformatics Center, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, 613 401, India. thamu@scbt.sastra.edu.

Funding

Department of Biotechnology (DBT), Government of India and SASTRA Deemed University BT/PR40144/BTIS/137/46/2022 and BT/PR40150/BTIS/137/81/2023
6 · The paper itself

Abstract

Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to identify the most promising candidates for a given drug target. In this study, the SARS-CoV-2 main protease (M

Indexed as

Antiviral AgentsCoronavirus 3C ProteasesProtease InhibitorsSARS-CoV-2Binding SitesCOVID-19COVID-19 Drug TreatmentDeep LearningDrug DesignGenerative Adversarial NetworksHumansLigandsMolecular Docking SimulationMolecular Dynamics SimulationProtein Binding3C-like proteinase, SARS-CoV-2Antiviral AgentsCoronavirus 3C ProteasesLigandsProtease Inhibitors2-benzyl-6-bromophenol scaffoldGAN frameworkInfectious diseaseMolecular dockingMolecular dynamicsSARS-CoV-2 Mpro

Identifiers

PMID39613993

What OpenQuestion holds

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Registered trials

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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.