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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

Local Anesthetics: Chemistry and Structure-Activity Relationship

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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...
1.0K
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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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative

Vinicius M Alves1,2, Alexander Golbraikh1, Stephen J Capuzzi1

  • 1Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.

Journal of Chemical Information and Modeling
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Multi-Descriptor Read Across (MuDRA) offers a fast, interpretable alternative to complex consensus Quantitative Structure-Activity Relationship (QSAR) models. This new method achieves high accuracy while enhancing model transparency and efficiency.

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Area of Science:

  • * Cheminformatics
  • * Computational Toxicology
  • * Predictive Modeling

Background:

  • * Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting chemical properties.
  • * Traditional consensus QSAR models often sacrifice interpretability for accuracy.
  • * Need for efficient and transparent predictive modeling approaches in drug discovery and chemical safety.

Purpose of the Study:

  • * Introduce Multi-Descriptor Read Across (MuDRA), a novel, interpretable QSAR modeling method.
  • * Benchmark MuDRA against conventional consensus QSAR models across diverse toxicological endpoints.
  • * Evaluate MuDRA's accuracy, interpretability, and computational efficiency.

Main Methods:

  • * Developed MuDRA, a method conceptually similar to kNN but utilizing multiple chemical descriptor types for similarity assessment.
  • * Built and validated MuDRA models for six key endpoints: Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption.
  • * Compared MuDRA performance with established consensus QSAR modeling techniques.

Main Results:

  • * MuDRA models demonstrated consistently high external predictive accuracy, comparable to conventional QSAR models.
  • * MuDRA significantly outperformed consensus models in transparency, interpretability, and computational speed.
  • * The method proved effective across a range of toxicological endpoints.

Conclusions:

  • * MuDRA presents a powerful, simple, and reliable alternative to complex consensus QSAR modeling.
  • * Its combination of accuracy, interpretability, and efficiency makes it valuable for cheminformatics and toxicology.
  • * MuDRA is freely available via the Chembench web portal, promoting wider adoption.