ArticleMolecules (Basel, Switzerland)2018
A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Machine Learning Identifies Potential Accessory Resistance-Associated Mutations in HIV-1 Integrase.Research square · 2026Article
- 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
- Factors Influencing the Binding of HIV-1 Protease Inhibitors: Insights from Machine Learning Models.ChemMedChem · 2025Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets.Viruses · 2023Article
- Article
- Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy.PeerJ · 2023Article
- Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.Journal of cheminformatics · 2022Article
- Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.Biomolecules · 2021Article
- Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.Molecular diversity · 2021Article
- Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.PloS one · 2021Article
- A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy.International journal of molecular sciences · 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
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Authors and funding
4 authors.
Funding
Abstract
The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and successfully used in new drug discovery. An emerging body of data regarding the interactions of small drug-like molecules with their protein targets provides the possibility of building models on "structure-property" relationships and analyzing the performance of various machine-learning techniques. In our research, we analyze several different types of descriptors in order to predict the resistance of HIV reverse transcriptase and protease to the marketed antiretroviral drugs using the Random Forest approach. First, we represented amino acid sequences as a set of short peptide fragments, which included several amino acid residues. Second, we represented nucleotide sequences as a set of fragments, which included several nucleotides. We compared these two approaches using open data from the Stanford HIV Drug Resistance Database. We have determined the factors that modulate the performance of prediction: in particular, we observed that the prediction performance was more sensitive to certain drugs than a type of the descriptor used.
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