ArticleBMC bioinformatics2018
Analysis of drug resistance in HIV protease.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Protein language model embeddings improve HIV drug resistance prediction: a comprehensive benchmark with attention-based interpretability.Bioinformatics (Oxford, England) · 2026Article
- Factors Influencing the Binding of HIV-1 Protease Inhibitors: Insights from Machine Learning Models.ChemMedChem · 2025Article
- Differentiated extracts from freshwater and terrestrial mollusks inhibit virulence factor production in Cryptococcus neoformans.Scientific reports · 2023Article
- Article
- Viral proteases as therapeutic targets.Molecular aspects of medicine · 2022Review
- Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison.Life (Basel, Switzerland) · 2022Article
- From Naturally-Sourced Protease Inhibitors to New Treatments for Fungal Infections.Journal of fungi (Basel, Switzerland) · 2021Review
- Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.Briefings in bioinformatics · 2021Review
- Evolution of drug resistance in HIV protease.BMC bioinformatics · 2020Article
- Darunavir-Resistant HIV-1 Protease Constructs Uphold a Conformational Selection Hypothesis for Drug Resistance.Viruses · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundDrug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies.
resultsThe machine learning produced highly accurate and robust classification of HIV protease resistance. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Generative machine learning models trained on one inhibitor could classify resistance from other inhibitors with varying levels of accuracy. Generally, the accuracy was best when the inhibitors were chemically similar.
conclusionsRestricted Boltzmann Machines are an effective machine learning tool for classification of genomic and structural data. They can also be used to compare resistance profiles of different protease inhibitors.
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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.