Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Viral Mutations

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...

Mutations

Mutations

Mutations

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

Solubility of Ionic Compounds

Solubility of Ionic Compounds

Solubility is the measure of the maximum amount of solute that can be dissolved in a given quantity of solvent at a given temperature and pressure. Solubility is usually measured in molarity (M) or moles per liter (mol/L). A compound is termed soluble if it dissolves in water.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A renally clearable tumor-targeted probe enabling functional delineation of tumor margins by NIR-II fluorescence lifetime imaging.

Materials today. Bio·2026

Same author

Novel Competitive, Nonpeptidic, SARS-CoV‑2 M<sup>pro</sup> Inhibitors with Improved Solubility.

ACS medicinal chemistry letters·2026

Same author

Identification and characterization of novel chikungunya virus polymerase inhibitors.

Journal of virology·2026

Same author

Interpretable Wavelet-CNN for Accurate Serum Raman Lung Cancer Diagnosis under Leakage-Safe, Patient-Level Splits.

Analytical chemistry·2026

Same author

Renal clearable exceedingly small amorphous iron oxide nanoclusters for high-resolution T<sub>1</sub> magnetic resonance.

Colloids and surfaces. B, Biointerfaces·2026

Same author

Discovery of Novel 2-Morpholine Tetrahydroisoquinoline CXCR4 Antagonists with Unique Properties.

Journal of medicinal chemistry·2026

Search research articles

Related Experiment Video

Updated: Feb 8, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active

Thomas M Kaiser¹, Pieter B Burger^1,2, Christopher J Butch^1,3

¹Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.

Journal of Chemical Information and Modeling

|June 29, 2018

Summary

Predicting HIV drug resistance is crucial for patient lifespans. Machine learning accurately forecasts compound susceptibility to HIV reverse transcriptase mutations, outperforming traditional methods.

More Related Videos

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Published on: April 9, 2014

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Related Experiment Videos

Last Updated: Feb 8, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

Published on: April 9, 2014

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Area of Science:

Virology
Computational Biology
Drug Discovery

Background:

HIV drug resistance threatens patient lifespans.
Predictive methods for resistance are needed for new drug development.

Purpose of the Study:

To develop a machine learning model for predicting HIV drug resistance.
To assess compound susceptibility to mutations in reverse transcriptase.

Main Methods:

Utilized Naïve Bayes Networks based on biological activities.
Targeted key reverse transcriptase residues (Y181, K103, L100).
Compared performance against traditional molecular mechanics.

Main Results:

Achieved high accuracy in predicting compound susceptibility to resistance.
Perfect retrospective prediction for K103 and Y181 mutant reverse transcriptase.
Machine learning approach outperformed molecular mechanics.

Conclusions:

Machine learning offers a powerful tool for predicting HIV drug resistance.
This method enhances drug discovery and broadens ML applications.
Accurate prediction aids in developing more effective antiretroviral therapies.