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Related Concept Videos

Factors Affecting Activity Coefficient01:17

Factors Affecting Activity Coefficient

The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...
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Secondary Active Transport

One example of how cells use the energy contained in electrochemical gradients is demonstrated by glucose transport into cells. The ion vital to this process is sodium (Na+), which is typically present in higher concentrations extracellularly than in the cytosol. Such a concentration difference is due, in part, to the action of an enzyme "pump" embedded in the cellular membrane that actively expels Na+ from a cell. Importantly, as this pump contributes to the high concentration of...
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Activity is the measure of the effective concentration of the species in solution. It can be expressed as the product of the molar concentration of the species and its activity coefficient. The activity coefficient is a dimensionless quantity and depends on the total ionic strength of the solution.
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Computational analysis of activity and selectivity cliffs.

Lisa Peltason1, Jürgen Bajorath

  • 1Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany.

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|September 15, 2010
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Summary
This summary is machine-generated.

This study introduces a new computational method to analyze how small molecule structure affects both potency and selectivity against related targets. This approach helps identify key compounds and structural features crucial for drug lead optimization.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Structure-activity relationships (SARs) are crucial for medicinal chemistry, typically focusing on compound potency against single targets.
  • Selectivity for related targets is a critical, yet often overlooked, parameter in optimizing drug leads.
  • Analyzing both potency and selectivity simultaneously presents a significant challenge in chemical lead optimization.

Purpose of the Study:

  • To present an integrative computational approach for systematically analyzing SARs and structure-selectivity relationships (SSRs) of small molecules.
  • To demonstrate the utility of this approach using a cathepsin inhibitor dataset.
  • To identify key compounds and structural features influencing compound selectivity and activity cliffs.

Main Methods:

  • Development of a computational methodology combining numerical scoring and graphical visualization of molecular networks.
  • Systematic analysis of SARs and SSRs to identify distinct local environments.
  • Comparative analysis of these environments to understand variable structure-potency-selectivity relationships.

Main Results:

  • The approach successfully identified different local SAR and SSR environments.
  • Variable relationships between molecular structure, potency, and selectivity were revealed through comparative analysis.
  • Key compounds contributing to activity or selectivity cliffs were identified, highlighting critical structural determinants of selectivity.

Conclusions:

  • The presented integrative approach provides a systematic way to analyze SARs and SSRs, crucial for medicinal chemistry.
  • Understanding local SAR and SSR environments aids in identifying compounds with desired potency and selectivity profiles.
  • This methodology facilitates the identification of structural features that drive compound selectivity, accelerating drug lead optimization.