ArticleJournal of chemical theory and computation2021
Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 21 citations in OpenAlex.
- Conformational asymmetry of human immunodeficiency virus-1 protease flaps increases along the darunavir drug resistance pathway.Protein science : a publication of the Protein Society · 2026Article
- Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning.The journal of physical chemistry. B · 2025Article
- Subtype-Specific HIV-1 Protease and the Role of Hinge and Flap Dynamics in Drug Resistance: A Subtype C Narrative.Viruses · 2025Review
- Next generation APOBEC3 inhibitors: optimally designed for potency and nuclease stability.Nucleic acids research · 2025Article
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- Article
- Ready to Eat Food: A Reason for Enhancement in Multidrug Resistance in Humans.Advanced pharmaceutical bulletin · 2024Article
- Next generation APOBEC3 inhibitors: Optimally designed for potency and nuclease stability.bioRxiv : the preprint server for biology · 2024Article
- Elucidating the Substrate Envelope of Enterovirus 68-3C Protease: Structural Basis of Specificity and Potential Resistance.Viruses · 2024Article
- Evolving Mutational Buildup in HIV-1 Protease Shifts Conformational Dynamics to Gain Drug Resistance.Journal of chemical information and modeling · 2023Article
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- Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions.Molecules (Basel, Switzerland) · 2022Review
- The evolution of the HIV-1 protease folding stability.Virus evolution · 2022Article
- Report of the National Institutes of Health SARS-CoV-2 Antiviral Therapeutics Summit.The Journal of infectious diseases · 2021Article
- Deciphering Antifungal Drug Resistance inJournal of chemical information and modeling · 2021Article
- Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond.Chemical reviews · 2021Review
- Viral proteases: Structure, mechanism and inhibition.The Enzymes · 2021Article
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 at 1 institution in 1 country.
Funding
Abstract
Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly understood and thus difficult to counteract. A machine learning strategy was developed that coupled parallel molecular dynamics simulations with experimental potency to identify specific conserved mechanisms underlying resistance. Physical features were extracted from the simulations, analyzed, and integrated into one consistent and interpretable elastic network model. To rigorously test this strategy, HIV-1 protease variants with diverse mutations were used, with potencies ranging from picomolar to micromolar to the drug darunavir. Feature reduction resulted in a model with four specific features that predicts for both the training and test sets inhibitor binding free energy within 1 kcal/mol of the experimental value over this entire range of potency. These predictive features are physically interpretable, as they vary specifically with affinity and diagonally transverse across the protease homodimer. This physics-based strategy of parallel molecular dynamics and machine learning captures mechanisms by which complex combinations of mutations confer resistance and identify critical features that serve as bellwethers of affinity, which will be critical in future drug design.
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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.