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Related Concept Videos

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...
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...
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.
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Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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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.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Related Experiment Video

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Exploring uncharted territories: predicting activity cliffs in structure-activity landscapes.

Rajarshi Guha1

  • 1NIH Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, Maryland 20850, USA.

Journal of Chemical Information and Modeling
|August 10, 2012
PubMed
Summary

This study introduces a predictive model for identifying molecular activity cliffs. The novel pairwise approach enables prospective identification of potential activity cliffs, aiding drug discovery efforts.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Activity cliffs, which represent structural changes impacting molecular activity, are crucial for understanding structure-activity relationships (SAR).
  • Existing methods for identifying activity cliffs are largely retrospective, limiting their utility in prospective drug design.
  • Predicting activity cliffs prospectively can guide the design of novel therapeutics with improved efficacy and safety profiles.

Purpose of the Study:

  • To develop a predictive model for identifying potential activity cliffs between molecules.
  • To enable prospective identification of molecules likely to exhibit activity cliffs within a dataset.
  • To overcome limitations of retrospective methods in activity cliff analysis.

Main Methods:

  • Utilized a pairwise approach to characterize molecular interactions and predict activity cliffs.
  • Developed random forest models trained on ChEMBL assay data and pairwise molecular descriptors.
  • Focused on predicting SALI (Structure-Activity Relationship Index) values for molecular pairs.

Main Results:

  • The developed models demonstrated reasonable Root Mean Square Error (RMSE) values in predicting SALI.
  • Surprisingly, model performance was better for predicting more significant activity cliffs.
  • The models showed an ability to prioritize molecules based on their propensity to form activity cliffs.

Conclusions:

  • The pairwise predictive model offers a novel tool for the prospective identification of activity cliffs.
  • This approach can guide medicinal chemists in designing molecules with desired activity profiles.
  • The method provides a valuable strategy for navigating SAR landscapes and optimizing lead compounds.