ArticleRSC advances2024
Identification of lead inhibitors for 3CLpro of SARS-CoV-2 target using machine learning based virtual screening, ADMET analysis, molecular docking and molecular dynamics simulations.
Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Machine Learning-Based High-Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors.ChemistryOpen · 2025Article
- Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning.Viruses · 2025Article
- AI-assisted discovery of potent FGFR1 inhibitors via virtual screening and in silico analysis.PloS one · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The SARS-CoV-2 3CLpro is a critical target for COVID-19 therapeutics due to its role in viral replication. We employed a screening pipeline to identify novel inhibitors by combining machine learning classification with similarity checks of approved medications. A voting classifier, integrating three machine learning classifiers, was used to filter a large database (∼10 million compounds) for potential inhibitors. This ensemble-based machine learning technique enhances overall performance and robustness compared to individual classifiers. From the screening, three compounds M1, M2 and M3 were selected for further analysis. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis compared these candidates to nirmatrelvir and azvudine. Molecular docking followed by 200 ns MD simulations showed that only M1 (6-[2,4-bis(dimethylamino)-6,8-dihydro-5
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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.