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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.
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Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
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Structure and Physical Properties of Alkynes02:37

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Introduction:
In nature, compounds containing both carbon and hydrogen are known as "hydrocarbons". Aliphatic hydrocarbons are compounds whose molecules contain saturated single bonds (i.e., alkanes) or unsaturated double or triple bonds. Alkenes contain carbon–carbon double bonds and have a structural formula CnH2n. Unsaturated hydrocarbons containing carbon–carbon triple bonds are called "alkynes" and are structurally represented by the formula CnH2n-2.
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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

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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.
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Building a Quantitative Structure-Property Relationship (QSPR) Model.

Robert D Clark1, Pankaj R Daga2

  • 1Simulations Plus, Inc., Lancaster, CA, USA. bob@simulations-plus.com.

Methods in Molecular Biology (Clifton, N.J.)
|March 9, 2019
PubMed
Summary
This summary is machine-generated.

Quantitative structure-property relationship (QSPR) models predict compound properties before synthesis. This work outlines a workflow for building, validating, and refining QSPR models, focusing on ADME properties.

Keywords:
ADMEData curationQSARQSPRRegression

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Toxicology

Background:

  • Understanding compound physicochemical and biochemical properties is crucial for predicting biological behavior.
  • Quantitative structure-property relationship (QSPR) models offer predictive capabilities based on molecular structure.

Purpose of the Study:

  • To describe a general workflow for developing robust QSPR models.
  • To detail data compilation, curation, model evaluation, and error analysis for QSPR.
  • To focus on absorption, distribution, metabolism, and excretion (ADME) properties while maintaining general applicability.

Main Methods:

  • Data compilation and curation for QSPR model development.
  • Performance evaluation of QSPR models.
  • Systematic analysis of predictive errors to identify data inconsistencies.

Main Results:

  • A comprehensive workflow for building reliable QSPR models is presented.
  • The methodology allows for prediction of compound properties prior to experimental synthesis.
  • Focus on ADME property prediction demonstrates the workflow's utility in drug and toxin research.

Conclusions:

  • The described workflow provides a robust framework for QSPR model development.
  • Accurate QSPR models are essential for predicting compound behavior in biological systems.
  • The approach is broadly applicable across various QSPR applications beyond ADME properties.