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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...
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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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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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Quasi-SMILES: quantitative structure-activity relationships to predict anticancer activity.

Alla P Toropova1, Andrey A Toropov2

  • 1Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri - IRCCS, Via La Masa 19, 20156, Milan, Italy. alla.toropova@marionegri.it.

Molecular Diversity
|October 12, 2018
PubMed
Summary

Predicting anticancer drug effectiveness using quasi-SMILES models offers a faster alternative to experiments. These models accurately forecast drug activity against various cancer cells, aiding pharmaceutical development.

Keywords:
Anticancer activityIsoquinoline quinonesMonte Carlo methodQSARQuasi-SMILES

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Experimental anticancer drug screening is time-consuming and costly.
  • Predictive modeling can accelerate the identification of potential anticancer agents.
  • Understanding structure-activity relationships is crucial for drug development.

Purpose of the Study:

  • To develop predictive models for the anticancer activity of isoquinoline quinones against different cell types.
  • To utilize quasi-SMILES descriptors for capturing molecular structure and cellular context.
  • To establish reliable algorithms for predicting anticancer potential.

Main Methods:

  • Quasi-SMILES representation to encode molecular structure and experimental conditions.
  • Monte Carlo techniques to determine optimal correlation weights for quasi-SMILES fragments.
  • Calculation of pIC50 values as a function of molecular structure and cell type.
  • Optimization using the index of ideality of correlation to enhance predictive power.

Main Results:

  • Developed predictive models for anticancer activity with good statistical quality.
  • Achieved a correlation coefficient range of 0.76-0.89 for external validation.
  • Identified stable structural promoters influencing anticancer activity (pIC50).

Conclusions:

  • Quasi-SMILES based models provide a reliable method for predicting anticancer activity.
  • The models demonstrate strong predictive potential and can guide drug design.
  • These computational approaches can improve the quality and efficiency of pharmaceutical agent development.