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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.9K
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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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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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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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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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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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Novel Methods for Prioritizing "Close-In" Analogs from Structure-Activity Relationship Matrices.

Liying Zhang1, Kjell Johnson2, Jeremy Starr3

  • 1Pfizer Global Research and Development , 610 Main Street, Cambridge, Massachusetts 02139, United States.

Journal of Chemical Information and Modeling
|June 29, 2017
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Novel structure-activity relationship matrix (SARM) methods enhance compound prioritization. Three new SARM approaches, including analysis of variance (ANV), show high predictive power for virtual compound selection in drug discovery.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Structure-Activity Relationship (SAR) data is crucial for drug design.
  • Traditional SAR analysis often lacks prospective application for analog prioritization.
  • The Structure-Activity Relationship Matrix (SARM) framework offers a scaffold/functional-group based approach to SAR pattern extraction.

Purpose of the Study:

  • To develop and evaluate novel prospective methods for compound prioritization using the SARM framework.
  • To introduce three new SARM-based prioritization strategies: matrix pattern-based, similarity-weighted matrix pattern-based, and analysis of variance (ANV).
  • To assess the predictive power of these novel methods on benchmark datasets.

Main Methods:

  • Development of three new compound prioritization algorithms based on the SARM framework.
  • Application and evaluation of these methods on six benchmark datasets.
  • Investigation of SARM parameter impact on prioritization performance.

Main Results:

  • All developed SARM-based prioritization methods demonstrated high predictive power (R² range: 0.63–0.82).
  • The analysis of variance (ANV) method outperformed a previously reported SARM-based method on five out of six datasets.
  • The study identified key SARM parameters influencing prioritization accuracy.

Conclusions:

  • The novel SARM-based prioritization methods offer robust and predictive tools for virtual compound selection.
  • The ANV method represents a significant advancement in SARM-based prospective prioritization.
  • These methods enhance the efficiency and success rate of early-stage drug design.