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

QSAR modeling using automatically updating correction libraries: application to a human plasma protein binding model.

Sarah L Rodgers1, Andrew M Davis, Nick P Tomkinson

  • 1AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire, United Kingdom. Sarah.Rodgers@AstraZeneca.com

Journal of Chemical Information and Modeling
|September 25, 2007
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

Antiviral Therapies for Adults With Mild to Moderate COVID-19 Infection.

JAMA·2026
Same author

Management of Crohn Disease in Adults.

JAMA·2026
Same author

Fertility Preservation in People With Cancer.

JAMA·2026
Same author

Management of Opioid Use Disorder.

JAMA·2026
Same author

Traditional Mediterranean physical activity: integration of active lifestyle behaviors and exercise with social interactions as part of daily life.

The Journal of sports medicine and physical fitness·2025
Same author

Late fluid flow in a primitive asteroid revealed by Lu-Hf isotopes in Ryugu.

Nature·2025

This study introduces a correction library to improve predictions for compounds, especially in human plasma protein binding models. Applying this library significantly enhances prediction accuracy compared to static or updating models.

Area of Science:

  • Computational chemistry
  • Pharmacokinetics
  • Drug discovery

Background:

  • Predictive models in drug discovery often face errors.
  • Compounds with similar properties tend to exhibit similar prediction errors.
  • Existing methods may not fully leverage newly acquired experimental data.

Purpose of the Study:

  • To apply a correction library approach to refine predictions for a human plasma protein binding model.
  • To assess the impact of incorporating new experimental data into predictive models.
  • To evaluate the time-dependent performance of the correction library.

Main Methods:

  • Development and application of a correction library using measured data.
  • Time-series simulations to analyze the library's temporal effectiveness.

Related Experiment Videos

  • Comparison of prediction accuracy against static and updating models.

    • Main Results:

    • Significant improvements in prediction accuracy were observed when using the correction library.
    • The correction library demonstrated superior performance over static models.
    • Performance gains were also noted in comparison to an updating model incorporating recent data.

    Conclusions:

    • A correction library is an effective strategy for enhancing the accuracy of compound property predictions.
    • This method offers a robust way to refine human plasma protein binding predictions.
    • The dynamic application of measured data via a correction library improves predictive modeling over time.