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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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...
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Local Anesthetics: Chemistry and Structure-Activity Relationship01:30

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Local anesthetics (LAs) are drugs that induce a temporary loss of sensation in a limited body area, preventing pain. Cocaine was the first local anesthetic discovered in the late 19th century. Cocaine is a benzoic acid ester obtained from the leaves of coca shrubs and was often used for its psychotropic effects. Cocaine was first isolated in 1860 by Albert Niemann. Sigmund Freud studied the physiological actions of cocaine. Carl Koller later introduced it into clinical practice in 1884 as a...
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Cholinergic Antagonists: Chemistry and Structure-Activity Relationship01:29

Cholinergic Antagonists: Chemistry and Structure-Activity Relationship

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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...
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Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

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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...
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Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:29

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

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

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

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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...
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Related Experiment Video

Updated: Jan 27, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Applications of machine learning-based quantitative structure activity relationship (ML-QSAR) models in environmental

Chao Chen1, Yujia Tan2, Yawei Liu1

  • 1School of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, Zhejiang, China; National & Local Joint Engineering Research Center for Ecological Treatment Technology of Urban Water Pollution, Wenzhou University, Wenzhou, 325035, China; Zhejiang Provincial Engineering Laboratory of Ecological Treatment Technology for Urban Water Pollution, Wenzhou, 325035, Zhejiang, China.

Environmental Research
|January 25, 2026
PubMed
Summary

Machine Learning-based Quantitative Structure-Activity Relationship (ML-QSAR) models offer a data-adaptive approach for environmental science, moving beyond traditional limitations. This shift enables more trustworthy, knowledge-integrated AI systems for pollutant assessment and risk management.

Keywords:
Environmental scienceMachine learningModeling paradigmQSARTrustworthy AI

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Area of Science:

  • Environmental Chemistry
  • Computational Toxicology
  • Cheminformatics

Background:

  • Traditional Quantitative Structure-Activity Relationship (QSAR) models face limitations due to data growth and chemical complexity in environmental domains.
  • There is a need for model-free, data-adaptive, and knowledge-integrated modeling paradigms.

Purpose of the Study:

  • To systematically review the development and application of Machine Learning-based QSAR (ML-QSAR) in environmental science.
  • To illustrate the paradigm shift from classical QSAR to AI systems integrating physical laws, conformal prediction, and chemical reasoning.

Main Methods:

  • Comprehensive review of ML-QSAR literature in environmental science.
  • Presentation of a full-lifecycle ML-QSAR modeling framework.
  • Exploration of the evolution toward autonomous, self-evolving scientific discovery cycles.

Main Results:

  • ML-QSAR enables a paradigm shift towards trustworthy, knowledge-integrated AI systems.
  • The review covers applications in predicting pollutant properties, environmental fate, ecological and human health risk assessment, and emerging contaminant identification.
  • A framework for ML-QSAR modeling from data curation to deployment is presented.

Conclusions:

  • ML-QSAR addresses limitations of traditional QSAR, offering enhanced capabilities for environmental data analysis.
  • Persistent challenges include data scarcity, model opacity, and regulatory alignment, requiring scientifically grounded solutions.
  • The future points towards next-generation environmental artificial intelligence and autonomous scientific discovery.