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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

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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

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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

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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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SPARFlow: a KNIME workflow for integrated structure-activity or structure-property relationship analysis.

Elier E Abreu-Martínez1, Karina Martinez-Mayorga2, Gabriel Merino3

  • 1Departamento de Física Aplicada, Centro de Investigación y de Estudios Avanzados Unidad Mérida, Km 6 Antigua Carretera a Progreso, Cordemex, 97310, Mérida, Yucatán, México.

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Summary
This summary is machine-generated.

We created SPARFlow, an open-source workflow for analyzing structure-activity relationships (SAR) and structure-property relationships (SPR). This tool aids in assessing chemical data suitability for predictive modeling by identifying activity cliffs and evaluating dataset modelability.

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Area of Science:

  • * Computational chemistry and cheminformatics.
  • * Drug discovery and development.
  • * Quantitative structure-activity relationship (QSAR) studies.

Background:

  • * Structure-activity relationship (SAR) and structure-property relationship (SPR) analyses are crucial for understanding molecular interactions and predicting compound behavior.
  • * Existing tools for SAR/SPR analysis are often fragmented, requiring integration of multiple software packages.
  • * Assessing dataset modelability and identifying activity cliffs are essential steps for successful predictive modeling in drug discovery.

Purpose of the Study:

  • * To develop an integrated, open-source workflow for comprehensive SAR/SPR analyses.
  • * To provide a unified platform within KNIME for data preprocessing, chemical curation, and SAR landscape characterization.
  • * To implement and update established metrics for assessing dataset modelability and identifying critical structural features.

Main Methods:

  • * Development of SPARFlow, an open-source KNIME workflow.
  • * Integration of modules for data preprocessing, chemical structure curation, similarity network construction, maximum common substructure detection, and R-group decomposition.
  • * Implementation of indices such as SALI, SARI, MODI*, and RMODI for SAR landscape characterization and modelability assessment.

Main Results:

  • * SPARFlow successfully integrates diverse SAR/SPR analysis techniques into a single KNIME pipeline.
  • * The workflow provides updated implementations of key metrics like MODI* and RMODI, alongside SARI and SALI.
  • * Validation across four diverse datasets (cruzain inhibitors, opioid agonists, pesticides, carbonyl compounds) demonstrated the workflow's applicability and robustness.

Conclusions:

  • * SPARFlow offers a valuable, unified solution for SAR/SPR analyses, enhancing efficiency and consistency.
  • * The workflow aids researchers in evaluating dataset suitability for predictive modeling and identifying key structural drivers of activity or properties.
  • * This open-source tool facilitates robust cheminformatics analyses in drug discovery and related fields.