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

The maximum common substructure as a molecular depiction in a supervised classification context: experiments in

B Cuissart1, F Touffet, B Crémilleux

  • 1Centre d'Etudes et de Recherche sur le Médicament de Normandie, UPRES EA 2126 5, rue Vaubénard, Université de Caen, France. cuissart@pharmacie.unicaen.fr

Journal of Chemical Information and Computer Sciences
|October 16, 2002
PubMed
Summary

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

Target fishing reveals PfPYK-1 and PfRab6 as potential targets of an antiplasmodial 4-anilino-2-trichloromethylquinazoline hit compound.

Bioorganic & medicinal chemistry·2024
Same author

Binding mode of Pyridoclax to myeloid cell leukemia-1 (Mcl-1) revealed by nuclear magnetic resonance spectroscopy, docking and molecular dynamics approaches.

Journal of biomolecular structure & dynamics·2019
Same author

Molecular basis of agonist docking in a human GPR103 homology model by site-directed mutagenesis and structure-activity relationship studies.

British journal of pharmacology·2014
Same author

Rapid and soft formulation of folate-functionalized nanoparticles for the targeted delivery of tripentone in ovarian carcinoma.

International journal of pharmaceutics·2013
Same author

Targeting the BH3 domain of Bcl-2 family proteins. A brief history from natural products to foldamers as promising cancer therapeutic avenues.

Current medicinal chemistry·2013
Same author

[Breast cancer on a scar of excision of ectopic breast tissue: a case report].

Gynecologie, obstetrique & fertilite·2011

This study demonstrates that Maximum Common Structure (MCS) based similarity indices effectively group compounds with similar activities. These findings support the development of classification models for quantitative structure/biodegradability relationships (QSBR).

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Biodegradability studies

Background:

  • Molecular similarity is crucial for grouping compounds with shared properties.
  • Quantitative Structure-Biodegradability Relationships (QSBR) utilize molecular descriptors to predict biodegradability.
  • Maximum Common Structure (MCS) provides a robust measure of molecular similarity.

Purpose of the Study:

  • To investigate the efficacy of MCS-based similarity indices for grouping compounds with similar activities.
  • To develop and evaluate classification models for QSBR using MCS-derived similarities.
  • To explore the performance of k-nearest-neighbor classifiers based on MCS.

Main Methods:

  • Calculating similarity indices exclusively based on the Maximum Common Structure (MCS).

Related Experiment Videos

  • Performing statistical tests to validate the grouping of compounds with similar activities.
  • Developing and evaluating classification models using MCS-based structural similarities.
  • Exploring a population of k-nearest-neighbor classifiers.

    • Main Results:

    • Statistical tests confirmed that MCS-based similarity indices significantly group compounds with similar biological activities.
    • The study successfully established classification models leveraging these structural similarities.
    • An in-depth analysis identified the best-performing classification models.

    Conclusions:

    • MCS-based similarity is a powerful tool for organizing chemical compounds based on activity.
    • The developed QSBR models demonstrate the utility of MCS in predicting biodegradability.
    • MCS and k-nearest-neighbor algorithms offer a promising approach for cheminformatics and drug discovery.