Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Combining chemodescriptors and biodescriptors in quantitative structure-activity relationship modeling.

Douglas M Hawkins1, Subhash C Basak, Jessica Kraker

  • 1School of Statistics, University of Minnesota, Minneapolis, 55455, USA.

Journal of Chemical Information and Modeling
|January 24, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Predicting mutagenicity of aromatic and heteroaromatic amine mutagens using a newly developed set of laplacian matrix-based graph invariants.

Acta chimica Slovenica·2026
Same author

Human Endothelial Membrane as a Structural Prototype: A Comparative Analysis with <i>Artemia salina</i> Endothelial-like Cell.

International journal of molecular sciences·2026
Same author

Chirobiophore: A Novel Framework for Quantifying Biochirality in Macromolecular Systems.

Biomolecules·2026
Same author

Precision Profile Weighted Deming Regression for Methods Comparison.

The journal of applied laboratory medicine·2026
Same author

Plasma pTau 217/β-amyloid 1-42 ratio for enhanced accuracy and reduced uncertainty in detecting amyloid pathology.

Brain : a journal of neurology·2026
Same author

Quantitative Structure-Activity Relationship (QSAR) Modeling of Chiral CCR2 Antagonists with a Multidimensional Space of Novel Chirality Descriptors.

Molecules (Basel, Switzerland)·2025
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
See all related articles

Halocarbon toxicity is a public health concern. Combining chemical and biological descriptors in Quantitative Structure-Activity Relationship (QSAR) models improves prediction of halocarbon liver toxicity compared to using either descriptor set alone.

Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Proteomics

Background:

  • Halocarbons are widely distributed environmental contaminants.
  • Their toxicity poses a significant public health risk.
  • Predicting chemical toxicity often relies on molecular descriptors.

Purpose of the Study:

  • To investigate the utility of biological descriptors derived from proteomic analysis in predicting halocarbon toxicity.
  • To compare the predictive performance of Quantitative Structure-Activity Relationship (QSAR) models using chemical descriptors, biological descriptors, or a combination of both.

Main Methods:

  • Hepatocytes were exposed to 14 halocarbons and a control.
  • Two-dimensional electrophoresis was used to analyze the expressed proteome and generate biological descriptors.

Related Experiment Videos

  • QSAR models were developed using ridge regression to predict eight toxicity measures.
  • Models were evaluated using chemical descriptors, biological descriptors, and combined descriptor sets.

    • Main Results:

    • Biological descriptors derived from proteomic data provided valuable information for toxicity prediction.
    • Models incorporating both chemical and biological descriptors generally outperformed models using either descriptor set alone.
    • The predictive accuracy varied depending on the specific toxicity endpoint being modeled.

    Conclusions:

    • Combining chemical and biological descriptors enhances the accuracy of halocarbon toxicity prediction models.
    • Proteomics-based biological descriptors show promise for improving toxicological assessments.
    • The developed methodology offers broad applicability for integrating chemical and biological data in predictive toxicology.