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

827
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...
827
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.1K
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...
1.1K
Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:22

Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

1.1K
Cholinergic agonists or cholinomimetics mimic the action of acetylcholine to stimulate the parasympathetic nervous system. They are categorized into direct-acting and indirect-acting agents. The direct-acting cholinergic drugs induce the parasympathetic response by directly binding to the muscarinic or nicotine receptors. In comparison, the indirect-acting cholinergic drugs prevent acetylcholine hydrolysis, indirectly contributing to the extended parasympathetic response.
The direct-acting...
1.1K
Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:29

Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

628
Indirect-acting cholinergic agonists are agents that interact with the acetylcholinesterase enzyme in the synaptic cleft, preventing the breakdown of acetylcholine into choline and acetate. Consequently, the concentration of acetylcholine in the synaptic cleft increases. These agonists can be classified into reversible and irreversible inhibitors based on their duration of action.
Reversible inhibitors display short to medium durations of action. Short-acting agents include simple alcohols with...
628
Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

3.2K
Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
3.2K
Cholinergic Antagonists: Chemistry and Structure-Activity Relationship01:29

Cholinergic Antagonists: Chemistry and Structure-Activity Relationship

2.3K
Cholinergic antagonists bind to cholinergic receptors and limit the effects of acetylcholine and other cholinergic agonists. Based on the specific cholinergic receptor affinity, these antagonists are classified as muscarinic or nicotinic. Anticholinergics interrupt parasympathetic innervations while sympathetic innervations remain uninterrupted. Muscarinic antagonists are also called 'muscarinic antagonists', 'antimuscarinics', or 'parasympatholytics'. Nicotinic...
2.3K

You might also read

Related Articles

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

Sort by
Same author

DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.

International journal of molecular sciences·2026
Same author

Chemical Motifs Associated with FAERS-Derived Severe Cutaneous Adverse Reaction Disproportionality Signals: An Interpretable Pharmacovigilance-Driven Cheminformatics Study.

International journal of molecular sciences·2026
Same author

Disproportionality Analysis and Timing of Drug-Associated Guillain-Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database.

Pharmaceuticals (Basel, Switzerland)·2026
Same author

Impact of Reporter Type on Signal Detection of Cancer Therapy-Induced Alopecia: A Hypothesis-Generating Study Using the FDA Adverse Event Reporting System.

Pharmaceuticals (Basel, Switzerland)·2026
Same author

Applications, image analysis, and interpretation of computer vision in medical imaging.

Frontiers in radiology·2026
Same author

Tumor-specific cytotoxicity of pyrazole-based chalcone derivatives in human oral squamous cell carcinoma cell lines.

Turkish journal of biology = Turk biyoloji dergisi·2025

Related Experiment Video

Updated: Aug 7, 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

1.4K

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.

Yasunari Matsuzaka1,2, Yoshihiro Uesawa1

  • 1Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan.

Molecules (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) models like DeepSNAP excel at predicting chemical compound activity by analyzing 3D structures. This advanced method surpasses traditional machine learning by automatically capturing complex features for higher accuracy.

Keywords:
DeepSNAPensemble learningneural networkpharmacokineticsregression model

More Related Videos

Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

10.0K
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

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

Related Experiment Videos

Last Updated: Aug 7, 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

1.4K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

10.0K
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

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

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Artificial intelligence in drug discovery

Background:

  • Quantitative structure-activity relationship (QSAR) models predict compound properties.
  • Traditional QSAR relies on manually selected molecular descriptors.
  • Deep learning (DL) offers powerful feature extraction but lacks interpretability.

Purpose of the Study:

  • To introduce and evaluate the DeepSNAP method for QSAR analysis.
  • To demonstrate DL's capability in automatically capturing 3D structural features.
  • To compare DeepSNAP performance against traditional descriptor-based machine learning.

Main Methods:

  • Utilized a molecular image-based deep learning approach (DeepSNAP).
  • Leveraged 3D chemical structure information for feature generation.
  • Employed deep neural networks with multiple hidden layers for complex pattern recognition.

Main Results:

  • DeepSNAP successfully captured spatial and temporal features from 3D compound structures.
  • The method built high-performance prediction models without manual feature engineering.
  • DeepSNAP outperformed traditional molecular descriptor-based machine learning methods.

Conclusions:

  • Deep learning, specifically DeepSNAP, provides a powerful alternative for QSAR analysis.
  • The method excels due to its ability to utilize 3D structural data and DL's feature discrimination.
  • DeepSNAP offers improved prediction accuracy and efficiency compared to conventional approaches.