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: then and now.

C D Selassie1, S B Mekapati, R P Verma

  • 1Pomona College, Claremont, California, USA. CRS04747@pomona.edu

Current Topics in Medicinal Chemistry
|December 10, 2002
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

Predictability of transcutaneous bilirubinometry in late preterm and term infants at risk for pathological hyperbilirubinemia.

Journal of neonatal-perinatal medicine·2020
Same author

Screening difficult-to-reach populations for tuberculosis using a mobile medical unit, Punjab India.

Public health action·2016
Same author

An in silico expert system for the identification of eye irritants.

SAR and QSAR in environmental research·2015
Same author

The allelic relationship of genes giving resistance to mungbean yellow mosaic virus in blackgram.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013
Same author

Effects of Tinospora cordifolia supplementation on semen quality and hormonal profile in rams.

Animal reproduction science·2013
Same author

Risk factors and clinical outcomes of pulmonary interstitial emphysema in extremely low birth weight infants.

Journal of perinatology : official journal of the California Perinatal Association·2006

This review traces the evolution of Quantitative Structure-Activity Relationship (QSAR) models, detailing electronic, hydrophobic, steric, and topological parameters. It highlights their application in drug design, particularly for non-nucleoside reverse transcriptase inhibitors.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Quantitative Structure-Activity Relationship (QSAR) analysis has evolved significantly since early observations by Crum-Brown and Frazier.
  • Key contributions from Hammett and Hansch established extrathermodynamic principles for modeling biological activity.
  • Modern QSAR models offer versatile applications, either independently or in conjunction with other analytical approaches.

Purpose of the Study:

  • To provide a comprehensive review of the historical development and current methodologies in QSAR.
  • To detail the standard classifications of QSAR parameters: electronic, hydrophobic, steric, and topological.
  • To illustrate the application of QSAR in drug design through comparative analyses.

Main Methods:

Related Experiment Videos

  • Tracing the historical evolution of QSAR principles and parameters.
  • Describing electronic parameters (e.g., Hammett sigma constants, dipole moments, quantum chemical indices).
  • Examining hydrophobicity parameters (historical, operational definitions, experimental and computational determination).
  • Detailing steric parameters (contributions from Taft, Hancock, Charton, Fujita, Verloop, Simon).
  • Reviewing topological indices (connectivity, kappa shape, electrotopological indices by Kier and Hall).
  • Presenting examples of QSAR models and comparative analyses of specific drug derivatives.

    • Main Results:

    • QSAR models integrate diverse parameters to predict and understand biological activity.
    • Electronic, hydrophobic, steric, and topological descriptors are crucial for model development.
    • Comparative QSAR analysis of non-nucleoside reverse transcriptase inhibitors (NNRTI's) provides mechanistic insights.
    • Specific examples include TIBO and HEPT derivatives, showcasing model utility in inhibitor design.

    Conclusions:

    • QSAR is a powerful paradigm for understanding and predicting biological activity.
    • The integration of various parameters enhances the predictive power and mechanistic understanding of QSAR models.
    • QSAR analysis is instrumental in guiding the rational design of novel therapeutic agents, including enzyme inhibitors.