ArticleBMC bioinformatics2008
Proteochemometric modeling of HIV protease susceptibility.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 72 citations in OpenAlex.
- Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.Journal of chemical information and modeling · 2025Article
- Flavoromics Approach in Critical Aroma Compounds Exploration of Peach: Correlation to Origin Based on OAV Combined with Chemometrics.Foods (Basel, Switzerland) · 2023Article
- Transgenerational metabolomic fingerprints in mice ancestrally exposed to the obesogen TBT.Environment international · 2021Article
- Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.Metabolomics : Official journal of the Metabolomic Society · 2021Article
- Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes.Molecules (Basel, Switzerland) · 2020Article
- An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models.Frontiers in molecular biosciences · 2020Article
- Proton NMR Enables the Absolute Quantification of Aqueous Metabolites and Lipid Classes in Unique Mouse Liver Samples.Metabolites · 2019Article
- New insights into the selective inhibition of the β-carbonic anhydrases of pathogenic bacteria Burkholderia pseudomallei and Francisella tularensis: a proteochemometrics study.Molecular diversity · 2019Article
- An Untargeted Metabolomics Approach to Investigate the Metabolic Modulations of HepG2 Cells Exposed to Low Doses of Bisphenol A and 17β-Estradiol.Frontiers in endocrinology · 2018Article
- BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions.Journal of cheminformatics · 2016Article
- Proteochemometric model for predicting the inhibition of penicillin-binding proteins.Journal of computer-aided molecular design · 2015Article
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- Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling.Journal of chemical biology · 2014Review
- Computational chemogenomics: is it more than inductive transfer?Journal of computer-aided molecular design · 2014Article
- Proteochemometric modeling in a Bayesian framework.Journal of cheminformatics · 2014Article
- Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets.Journal of cheminformatics · 2013Article
- Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets.Journal of cheminformatics · 2013Article
- Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.PLoS computational biology · 2013Article
- Screening of selective histone deacetylase inhibitors by proteochemometric modeling.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.
resultsThe model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72.
conclusionOur results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.
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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.