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Related Concept Videos

Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

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 the aromatic...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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 its...
Physical Properties of Amines01:26

Physical Properties of Amines

Amines with low molecular weight are usually gaseous at room temperature, while those with high molecular weight are liquid or solids in nature. Usually, low molecular weight amines have a rotten fish-like smell. Diamines typically have a pungent smell. For instance, cadaverine and putrescine, depicted in Figure 1, are two molecules responsible for decaying tissue.
Nomenclature of Aryl and Heterocyclic Amines01:10

Nomenclature of Aryl and Heterocyclic Amines

The simplest aromatic amine is phenylamine, which contains an –NH2 functionality directly attached to an aromatic ring. The name aniline is designated for this skeleton. As shown in Figure 1, the common names of the functionalized anilines involve prefixes ortho-, meta-, and para- to indicate the substitution position. Different functionalized aniline derivatives also have notable trivial names.
Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:29

Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

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...
Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:22

Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

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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Color Spot Test As a Presumptive Tool for the Rapid Detection of Synthetic Cathinones
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Structure-activity correlations for illicit amphetamines using ANN and constitutional descriptors.

S Gosav1, M Praisler, D O Dorohoi

  • 1Department of Physics, (")Dunărea de Jos" University, Str. Domneasca nr. 47, Galaţi, Romania.

Talanta
|October 31, 2008
PubMed
Summary
This summary is machine-generated.

This study developed artificial neural networks (ANNs) to predict the biological activity of new amphetamine-like substances. The systems classify drugs by toxicological activity for forensic and clinical applications.

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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Area of Science:

  • Computational Chemistry
  • Toxicology
  • Pharmacology

Background:

  • Illicit substances pose significant public health and forensic challenges.
  • Accurate identification of novel psychoactive substances is crucial for risk assessment.
  • Predictive toxicology systems can aid in evaluating new chemical entities.

Purpose of the Study:

  • To develop an expert system using artificial neural networks (ANNs) to predict the biological activity of novel amphetamine-like compounds.
  • To classify amphetamines based on toxicological activity (stimulant vs. hallucinogenic) and differentiate them from non-amphetamine substances.
  • To create a system for epidemiological, clinical, and forensic analysis of new molecular structures.

Main Methods:

  • Two types of ANNs were designed: a spectral ANN using infrared (IR) spectral data and a structural ANN (CD-ANN) using constitutional descriptors (CDs).
  • The spectral ANN utilized 100 key absorption intensities from normalized GC-FT-IR spectra of drugs of abuse and related compounds.
  • The CD-ANN incorporated 45 computed CDs per sample, with optimizations to the training set and input variables.

Main Results:

  • Both spectral and structural ANNs were trained to classify amphetamines by toxicological activity.
  • The CD-ANN's efficiency was enhanced through training set optimization and an increased number of input variables.
  • A comparative analysis of the performance of the spectral and structural networks was conducted.

Conclusions:

  • Artificial neural networks can be effectively employed to predict the biological and toxicological activity of new substances.
  • The developed expert system provides a valuable tool for the identification and classification of amphetamine-related compounds.
  • This approach supports forensic, clinical, and epidemiological investigations involving novel psychoactive substances.