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

Using theoretical descriptors in quantitative structure-activity relationships: some toxicological indices.

L Y Wilson1, G R Famini

  • 1Department of Chemistry, Loma Linda University Riverside, California 92515.

Journal of Medicinal Chemistry
|May 1, 1991
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

Modeling drug-melanin interaction with theoretical linear solvation energy relationships.

Pigment cell research·1997
Same author

Visualization of electronic properties of molecules in chemical reactions.

Journal of molecular graphics·1995
Same author

Solubility properties in polymers and biological media 5: an analysis of the physicochemical properties which influence octanol-water partition coefficients of aliphatic and aromatic solutes.

Journal of pharmaceutical sciences·1985
Same author

Strategy can close the "capital gap".

Hospitals·1982

Computational medicinal chemistry utilizes quantitative structure-activity relationships (QSAR) for predicting drug properties. This study introduces theoretically determined parameters for linear solvation energy relationships (LSER), improving a priori prediction accuracy for biological activities.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Quantitative Structure-Activity Relationships (QSAR) are widely used in medicinal chemistry to link molecular structure to biological activity.
  • Traditional QSAR methods face challenges with parameter set uniformity and cross-dataset analysis.
  • Linear Solvation Energy Relationships (LSER) offer a more unified approach using a single parameter set for diverse properties.

Purpose of the Study:

  • To address limitations in QSAR by replacing empirical LSER parameters with theoretically derived ones.
  • To enable more accurate *a priori* prediction of chemical and biological properties.
  • To validate the utility of theoretical parameters in medicinal chemistry applications.

Main Methods:

  • Development and application of theoretically determined parameters to replace traditional LSER solvatochromatic parameters.

Related Experiment Videos

  • Comparative analysis of theoretical versus empirical LSER parameters.
  • Evaluation across five distinct biological activity datasets.

    • Main Results:

    • Theoretically determined parameters demonstrate an excellent fit when compared to empirical LSER parameters.
    • The new parameter set facilitates improved *a priori* prediction capabilities.
    • Successful application across multiple biological activity datasets validates the approach.

    Conclusions:

    • Theoretical parameterization of LSER provides a robust and accurate alternative to empirical methods in medicinal chemistry.
    • This advancement enhances the predictive power of computational approaches for drug design.
    • The study highlights the potential of *in silico* methods for accelerating drug discovery and development.