ArticleMolecules (Basel, Switzerland)2025
An Interpretable Deep Learning and Molecular Docking Framework for Repurposing Existing Drugs as Inhibitors of SARS-CoV-2 Main Protease.
Article in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Integrated Metabolomics and Multilevel Validation of the Anti-Inflammatory and Skin-Soothing Effects of Bioactive Components fromInternational journal of molecular sciences · 2026Article
- MSCMF-DTB: a multi-scale cross-modal fusion framework for drug-target binding prediction.Scientific reports · 2026Article
- Computational Investigation of Flavonoid-Associated Molecular Pathways in Astrogliosis Modulation.The Yale journal of biology and medicine · 2026Article
- Challenges and Opportunities in Multi-Omics Data Acquisition and Analysis: Toward Integrative Solutions.Biomolecules · 2026Review
- Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Despite the widespread use of vaccines against SARS-CoV-2, COVID-19 continues to pose global health challenges, requiring efficient drug screening and repurposing strategies. This study presents a novel hybrid framework that integrates deep learning (DL) with molecular docking to accelerate the identification of potential therapeutics. The framework comprises three crucial steps: (1) a previously developed DL model is employed to rapidly screen candidate compounds, selecting those with predicted interaction scores above a cut-off value of 0.8; (2) AutoDock Vina version 1.5.6 and LeDock version 1.0 are used to evaluate binding affinities, with a threshold of <-7.0 kcal·mol
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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.