ArticleChemical physics2023
Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.
Article in Chemical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Searching potential GSK-3β inhibitors from marine sources using atomistic simulations.Molecular diversity · 2026Article
- Machine Learning Meets Physics-based Modeling: A Mass-spring System to Predict Protein-ligand Binding Affinity.Current medicinal chemistry · 2025Review
- Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.Royal Society open science · 2024Article
- MedChemExpress compounds prevent neuraminidase N1RSC advances · 2024Article
- Article
- Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.Molecular diversity · 2024Article
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of
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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.