ArticleBMC genomics2014
Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Machine Learning Identifies Potential Accessory Resistance-Associated Mutations in HIV-1 Integrase.Research square · 2026Article
- Factors Influencing the Binding of HIV-1 Protease Inhibitors: Insights from Machine Learning Models.ChemMedChem · 2025Article
- Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes.Life (Basel, Switzerland) · 2025Review
- Article
- Revertant mutation V48G alters conformational dynamics of highly drug resistant HIV protease PRS17.Journal of molecular graphics & modelling · 2021Article
- Novel HIV PR inhibitors with C4-substituted bis-THF and bis-fluoro-benzyl target the two active site mutations of highly drug resistant mutant PRBiochemical and biophysical research communications · 2021Article
- Using machine learning and big data to explore the drug resistance landscape in HIV.PLoS computational biology · 2021Article
- Review
- Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.PloS one · 2021Article
- Evolution of drug resistance in HIV protease.BMC bioinformatics · 2020Article
- Highly drug-resistant HIV-1 protease reveals decreased intra-subunit interactions due to clusters of mutations.The FEBS journal · 2020Article
- An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.Frontiers in chemistry · 2020Article
- HIV drug resistance prediction with weighted categorical kernel functions.BMC bioinformatics · 2019Article
- Highly Drug-Resistant HIV-1 Protease Mutant PRS17 Shows Enhanced Binding to Substrate Analogues.ACS omega · 2019Article
- Analysis of drug resistance in HIV protease.BMC bioinformatics · 2018Article
- Decoding HIV resistance: from genotype to therapy.Future medicinal chemistry · 2017Review
- Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks.BMC bioinformatics · 2017Article
- Automated prediction of HIV drug resistance from genotype data.BMC bioinformatics · 2016Article
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDrug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy.
resultsIn order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance.
conclusionThe approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.
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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.