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

Structure-based classification of antibacterial activity.

Mark T D Cronin1, Aynur O Aptula, John C Dearden

  • 1School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England. m.t.cronin@livjm.ac.uk

Journal of Chemical Information and Computer Sciences
|July 23, 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

Traumatic Brain Injury in an Elite Adolescent Soccer Goalkeeper: A Case Report.

Cureus·2026
Same author

Efficacy and safety of retatrutide, a GIP, GLP-1, and glucagon receptor agonist, in people with type 2 diabetes and inadequate glycaemic control with diet and exercise (TRANSCEND-T2D-1): a double-blind, randomised, phase 3 trial.

Lancet (London, England)·2026
Same author

Evaluating success rates of rotary NiTi versus reciprocating files in endodontic treatment.

Bioinformation·2026
Same author

AAV-mediated gene transfer of a novel microdystrophin ameliorates pathology and enhances muscle function in a mouse model of DMD.

Molecular therapy. Nucleic acids·2026
Same author

Tirzepatide as an Add-on for Participants with Inadequate Glycemic Control Using Basal Insulin: Pooled Subgroup Analysis of SURPASS-5 and -6.

Diabetes therapy : research, treatment and education of diabetes and related disorders·2026
Same author

Development of the Patient Satisfaction with Medication for Diabetes (PSMD) questionnaire.

Journal of patient-reported outcomes·2026
Same journal

Future Papers.

Journal of chemical information and computer sciences·2016
Same journal

Future Papers.

Journal of chemical information and computer sciences·2016
Same journal

Future Papers.

Journal of chemical information and computer sciences·2016
Same journal

Future Papers.

Journal of chemical information and computer sciences·2016
Same journal

Future Papers.

Journal of chemical information and computer sciences·2016
Same journal

Future Papers.

Journal of chemical information and computer sciences·2016
See all related articles

This study developed a quantitative structure-activity relationship (QSAR) model to predict antibacterial activity. Six key molecular descriptors accurately classify compounds, enabling efficient in silico screening for new drugs.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Antibacterial drug discovery is crucial due to rising resistance.
  • In silico screening methods accelerate the identification of potential drug candidates.
  • Quantitative Structure-Activity Relationship (QSAR) models offer a computational approach to predict biological activity.

Purpose of the Study:

  • To develop a simple QSAR model for classifying and predicting antibacterial activity.
  • To enable efficient in silico screening of compounds for antibacterial properties.
  • To identify key molecular descriptors influencing antibacterial efficacy.

Main Methods:

  • Analysis of a database containing 661 compounds with known antibacterial activity status.
  • Calculation of 167 physicochemical and structural descriptors for each compound.

Related Experiment Videos

  • Application of Analysis of Variance (ANOVA), linear discriminant analysis, and binary logistic regression.
  • Validation of model predictivity using a randomly selected test set (30% of compounds).

    • Main Results:

    • Six molecular descriptors related to hydrophobicity and hydrogen bonding significantly separated active from inactive compounds.
    • Both linear discriminant and binary logistic regression models demonstrated good predictive performance.
    • Logistic regression analysis provided slightly higher accuracy in modeling the data compared to discriminant analysis.

    Conclusions:

    • A robust QSAR model was successfully developed for predicting antibacterial activity.
    • The model effectively utilizes hydrophobicity and hydrogen bonding descriptors for compound classification.
    • This approach facilitates rapid in silico screening for novel antibacterial agents.