ReviewFuture medicinal chemistry2017
Decoding HIV resistance: from genotype to therapy.
Review in Future medicinal chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Machine Learning Identifies Potential Accessory Resistance-Associated Mutations in HIV-1 Integrase.Research square · 2026Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Identification of 2-(4-N,N-Dimethylaminophenyl)-5-methyl-1-phenethyl-1H-benzimidazole targeting HIV-1 CA capsid protein and inhibiting HIV-1 replication in cellulo.BMC pharmacology & toxicology · 2022Article
- Antiviral therapies: advances and perspectives.Fundamental & clinical pharmacology · 2021Review
- Evolution of drug resistance in HIV protease.BMC bioinformatics · 2020Article
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Genetic variation in HIV poses a major challenge for prevention and treatment of the AIDS pandemic. Resistance occurs by mutations in the target proteins that lower affinity for the drug or alter the protein dynamics, thereby enabling viral replication in the presence of the drug. Due to the prevalence of drug-resistant strains, monitoring the genotype of the infecting virus is recommended. Computational approaches for predicting resistance from genotype data and guiding therapy are discussed. Many prediction methods rely on rules derived from known resistance-associated mutations, however, statistical or machine learning can improve the classification accuracy and assess unknown mutations. Adding classifiers such as information on the atomic structure of the protein can further enhance the predictions.
Indexed as
Identifiers
What OpenQuestion holds
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.