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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

735
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...
735
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.0K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.0K
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.0K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
8.0K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

383
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
383
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

3.2K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
3.2K
Drug Regulation01:25

Drug Regulation

1.4K
Drug regulation encompasses the management of drug usage by evaluating its safety and efficacy through assessments conducted by regulatory authorities. Regrettably, the history of drug regulation is marred by several catastrophic events. One such incident is the Elixir Sulfanilamide tragedy, in which the toxic compound diethyl glycol was included in a sweet-tasting medication, leading to numerous fatalities. This event prompted the enactment of the Food, Drug, and Cosmetic Act in 1938. Under...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Cyclosporin A toxicity on endothelial cells differentiated from induced pluripotent stem cells: Assembling an adverse outcome pathway.

Toxicology in vitro : an international journal published in association with BIBRA·2024
Same author

Targeting <i>N</i>-Acetylglucosaminidase in <i>Staphylococcus aureus</i> with Iminosugar Inhibitors.

Antibiotics (Basel, Switzerland)·2024
Same author

Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

International journal of molecular sciences·2024
Same author

Exploring the dynamics of the ABCB1 membrane transporter P-glycoprotein in the presence of ATP and active/non-active compounds through molecular dynamics simulations.

Toxicology·2024
Same author

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

International journal of molecular sciences·2023
Same author

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods.

International journal of molecular sciences·2022

Related Experiment Video

Updated: Jul 12, 2025

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

1.2K

Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use.

Marjana Novič1

  • 1Theory Department, National Institute of Chemistry, Ljubljana, Slovenia.

Current Topics in Medicinal Chemistry
|October 23, 2023
PubMed
Summary

Quantitative Structure-Activity Relationship (QSAR) modeling leverages machine learning for chemical data analysis. This review highlights QSAR principles, data curation, and artificial neural networks for predicting biological properties.

Keywords:
Artificial neural networksClassifiersCounter-propagationDeep learningDrug developmentMolecular descriptorsPredictive modelsQSARToxicity assessment.

More Related Videos

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.0K
Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons
15:05

Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons

Published on: February 5, 2015

9.4K

Related Experiment Videos

Last Updated: Jul 12, 2025

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

1.2K
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.0K
Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons
15:05

Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons

Published on: February 5, 2015

9.4K

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Toxicology

Background:

  • Quantitative Structure-Activity Relationship (QSAR) modeling is crucial in drug discovery and regulatory science.
  • Recent advancements in data collection and machine learning (ML) have enhanced QSAR capabilities.
  • The integration of artificial intelligence (AI) tools is transforming data-driven modeling approaches.

Approach:

  • This review details the core principles of QSAR modeling.
  • It emphasizes data assembly, curation, and molecular descriptor computation.
  • The process includes model optimization, validation, and defining the scope of applicability.

Key Points:

  • Artificial neural networks (ANNs) are effective nonlinear methods for QSAR.
  • ANNs excel at extracting complex patterns from large datasets.
  • These models can accurately classify new chemicals and predict biological activities.

Conclusions:

  • QSAR modeling, particularly with ANNs, offers powerful insights into chemical-biological interactions.
  • Data-driven approaches are essential for advancing predictive toxicology and drug design.
  • The review underscores the importance of robust QSAR model development and validation.