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Related Concept Videos

Solubility Equilibria03:07

Solubility Equilibria

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Solubility equilibria are established when the dissolution and precipitation of a solute species occur at equal rates. These equilibria underlie many natural and technological processes, ranging from tooth decay to water purification. An understanding of the factors affecting compound solubility is, therefore, essential to the effective management of these processes. This section applies previously introduced equilibrium concepts and tools to systems involving dissolution and precipitation.
The...
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Solubility Equilibria: Overview01:09

Solubility Equilibria: Overview

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When a substance such as sodium chloride is added to water, it dissolves, forming an aqueous solution. The extent of dissolution is called solubility. The process of dissolution can exist in equilibrium, just like other chemical processes. Solubility equilibria are also called precipitation equilibria because the process of solubility can be reversible. The reverse of the solubility process is called precipitation.
Solubility is important in biological and environmental processes. A notable...
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Solution Formation02:16

Solution Formation

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There is no one solvent that can dissolve every type of solute. Some substances that readily dissolve in a certain solvent might be insoluble in a different solvent. A simple way to predict which substances dissolve in which solvent is the phrase "like dissolves like". This means that polar substances, such as salt and sugar, dissolve in a polar substance like water. In contrast, non-polar substances are more soluble in non-polar solvents such as carbon tetrachloride.
This selective...
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Factors Affecting Dissolution: Drug pKa, Lipophilicity and GI pH01:21

Factors Affecting Dissolution: Drug pKa, Lipophilicity and GI pH

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Drug absorption within the gastrointestinal (GI) tract is a complex process influenced by several critical factors, including the site pH, the drug's dissociation constant (pKa), and the drug's lipophilicity. The GI tract exhibits a pH gradient, with an acidic environment in the stomach and a more alkaline environment in the small intestine. This pH variation directly affects the ionization state of drugs.
A drug's pKa and the pH of the gastrointestinal (GI) tract play crucial roles...
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Factors Affecting Solubility04:01

Factors Affecting Solubility

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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
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Physical Properties Affecting Solubility02:19

Physical Properties Affecting Solubility

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Solutions of Gases in Liquids
As for any solution, the solubility of a gas in a liquid is affected by the attractive intermolecular forces between solute and solvent species. Unlike solid and liquid solutes, however, there is no solute-solute intermolecular attraction to overcome when a gaseous solute dissolves in a liquid solvent since the atoms or molecules comprising a gas are far separated and experience negligible interactions. Consequently, solute-solvent interactions are the sole...
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Updated: Jun 30, 2025

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
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Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid

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Will we ever be able to accurately predict solubility?

P Llompart1,2, C Minoletti2, S Baybekov1

  • 1Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France.

Scientific Data
|March 19, 2024
PubMed
Summary
This summary is machine-generated.

Predicting thermodynamic solubility with machine learning is difficult. This study reveals that current models falter due to data issues and overlooked historical datasets, impacting their real-world reliability.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Physical chemistry

Background:

  • Machine learning models for thermodynamic solubility prediction show promise but often fail prospectively.
  • Discrepancies in model reliability stem from data quality and historical context.

Purpose of the Study:

  • Investigate reasons for poor prospective performance of machine learning solubility models.
  • Analyze the aqueous solubility data landscape and assess data quality over 20 years.
  • Develop a data curation workflow for improved solubility modeling.

Main Methods:

  • Conducted a historical review of solubility datasets and models.
  • Benchmarked recent machine learning models on a newly curated dataset.
  • Analyzed factors influencing model utility, including interlaboratory variation and solute ionic state.

Main Results:

  • Identified significant overlaps and overlooked datasets in historical solubility data.
  • Observed poor performance of recent state-of-the-art models on curated data.
  • Highlighted that models lack defined applicability domains and ignore historical data.

Conclusions:

  • Current machine learning solubility models are not robust for public use.
  • Data quality, historical data integration, and defined applicability domains are crucial for reliable models.
  • Publicly available, quality-assessed datasets and improved models are provided.