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 Concept Videos

Antifungal Agents01:15

Antifungal Agents

Amphotericin B is a broad-spectrum antifungal agent that exploits structural differences between fungal and mammalian cell membranes. Its amphipathic structure—featuring a hydrophobic polyene-lactone ring and a hydrophilic region containing mycosamine and carboxylic acid groups—enables selective binding to ergosterol, a sterol predominantly found in fungal plasma membranes. This selective interaction underlies the drug’s antifungal activity, although weak binding to cholesterol contributes to...
Five-Membered Heterocyclic Aromatic Compounds: Overview01:13

Five-Membered Heterocyclic Aromatic Compounds: Overview

Heterocyclic aromatic compounds are cyclic compounds that are aromatic and have one or more heteroatoms—atoms other than carbon, in the ring. Depending upon the number of atoms present in the ring, they can be either five or six-membered. Examples of five-membered heterocyclic aromatic compounds include pyrrole, furan, thiophene, and imidazole. Pyrrole consists of one nitrogen atom having one lone pair of electrons. Furan and thiophene have one oxygen and one sulfur heteroatom, respectively.
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Basicity of Heterocyclic Aromatic Amines01:25

Basicity of Heterocyclic Aromatic Amines

Heterocyclic amines, where the N atom is a part of an alicyclic system, are similar in basicity to alkylamines. Interestingly, the heterocyclic amine having a nitrogen atom as part of an aromatic ring has much less basicity than its corresponding alicyclic counterpart. For this reason, as presented in Figure 1, piperidine (pKb = 2.8) is significantly more basic than pyridine (pKb = 8.8).
Criteria for Aromaticity and the Hückel 4n + 2 Rule01:20

Criteria for Aromaticity and the Hückel 4n + 2 Rule

Like benzene, cyclobutadiene and cyclooctatetraene are cyclic compounds with alternate single and double bonds. However, their chemical behavior differs from benzene, as they are unstable and not aromatic. So, what are the structural characteristics of unsaturated compounds categorized as aromatic?
For the first time, Eric Hückel, a German chemical physicist, derived a set of structural features for a compound to be classified as aromatic. This is now known as Hückel’s rule or the 4n + 2 rule.
Nomenclature of Aryl and Heterocyclic Amines01:10

Nomenclature of Aryl and Heterocyclic Amines

The simplest aromatic amine is phenylamine, which contains an –NH2 functionality directly attached to an aromatic ring. The name aniline is designated for this skeleton. As shown in Figure 1, the common names of the functionalized anilines involve prefixes ortho-, meta-, and para- to indicate the substitution position. Different functionalized aniline derivatives also have notable trivial names.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Linguistic markers of emotional reactivity and their association with anxiety, depression, and stress among emergency call takers and dispatchers.

PloS one·2026
Same author

Barbatolic Acid Prevents Tau and Amylin Interaction and Stimulates the Growth of Acetylated Microtubules in Cell Culture.

Current drug targets·2026
Same author

A juglone derivative that disrupts mitochondrial redox metabolism, inhibiting the breast fibroblast-cancer cell pro-migratory signaling induced by doxorubicin.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026
Same author

From bioactivity prediction to experimental protocol evaluation: QSAR models on the anticarcinogenic activity of flavonoids and related compounds in MCF-7 breast cancer models.

Journal of computer-aided molecular design·2026
Same author

Computational modeling of ubiquitin specific protease 7 (USP7) complexes with N-benzylpiperidinol derivatives incorporating binding site flexibility.

Journal of molecular graphics & modelling·2026
Same author

Human Implementation of Upper Extremity Amputation Incorporating Agonist-Antagonist Myoneural Interface Construction.

Plastic and reconstructive surgery·2025

Related Experiment Video

Updated: Jul 17, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

QSAR analysis for heterocyclic antifungals.

Pablo R Duchowicz1, Martín G Vitale, Eduardo A Castro

  • 1Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), División Química Teórica, Departamento de Química, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, 1900 La Plata, Argentina. prduchowicz@yahoo.com.ar

Bioorganic & Medicinal Chemistry
|February 14, 2007
PubMed
Summary

This study developed a quantitative structure-activity relationship (QSAR) model to predict antifungal activity against Candida albicans. The model accurately forecasts the potency of novel heterocyclic compounds.

More Related Videos

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Synthesis and Assay of Vibrio Quorum Sensing Inhibitors
03:29

Synthesis and Assay of Vibrio Quorum Sensing Inhibitors

Published on: May 31, 2024

Related Experiment Videos

Last Updated: Jul 17, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Synthesis and Assay of Vibrio Quorum Sensing Inhibitors
03:29

Synthesis and Assay of Vibrio Quorum Sensing Inhibitors

Published on: May 31, 2024

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Mycology

Background:

  • Candida albicans infections pose a significant public health challenge, necessitating the development of novel antifungal agents.
  • Understanding the relationship between molecular structure and antifungal activity is crucial for drug discovery.
  • Heterocyclic compounds are a promising class of molecules with potential antifungal properties.

Purpose of the Study:

  • To establish a robust quantitative structure-activity relationship (QSAR) model for predicting antifungal potencies.
  • To identify key molecular descriptors influencing the activity of heterocyclic derivatives against Candida albicans.
  • To apply the developed QSAR model for the virtual screening of new compounds.

Main Methods:

  • Linear regression analysis was employed using 1202 numerical descriptors.
  • These descriptors represent topological, geometrical, and electronic molecular structure aspects.
  • The model was developed using data from 96 known heterocyclic ring derivatives.

Main Results:

  • A statistically significant QSAR model was achieved, correlating molecular structure with antifungal activity.
  • The model effectively captured the structure-activity relationships for the tested compounds.
  • The predictive capability of the model was validated by its successful application to 60 unmeasured compounds.

Conclusions:

  • The developed QSAR model serves as a valuable tool for predicting antifungal activity.
  • This approach can accelerate the discovery of new and effective antifungal agents against Candida albicans.
  • The study highlights the importance of molecular descriptors in understanding antifungal drug efficacy.