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Related Concept Videos

Factors Affecting Solubility04:01

Factors Affecting Solubility

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:
Intermolecular Forces in Solutions02:28

Intermolecular Forces in Solutions

The formation of a solution is an example of a spontaneous process, a process that occurs under specified conditions without energy from some external source.
When the strengths of the intermolecular forces of attraction between solute and solvent species in a solution are no different than those present in the separated components, the solution is formed with no accompanying energy change. Such a solution is called an ideal solution. A mixture of ideal gases (or gases such as helium and argon,...
Solubility03:00

Solubility

Solution, Solubility, and Solubility Equilibrium
A solution is a homogeneous mixture composed of a solvent, the major component, and a solute, the minor component. The physical state of a solution—solid, liquid, or gas—is typically the same as that of the solvent. Solute concentrations are often described with qualitative terms such as dilute (of relatively low concentration) and concentrated (of relatively high concentration).
In a solution, the solute particles (molecules, atoms, and/or ions)...
Aqueous Solutions and Heats of Hydration02:42

Aqueous Solutions and Heats of Hydration

Water and other polar molecules are attracted to ions. The electrostatic attraction between an ion and a molecule with a dipole is called an ion-dipole attraction. These attractions play an important role in the dissolution of ionic compounds in water.
When ionic compounds dissolve in water, the ions in the solid separate and disperse uniformly throughout the solution because water molecules surround and solvate the ions, reducing the strong electrostatic forces between them. This process...
Solubility Equilibria: Overview01:09

Solubility Equilibria: Overview

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...
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...

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Updated: May 11, 2026

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water
16:24

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

Published on: August 2, 2012

Aqueous solubility prediction: do crystal lattice interactions help?

Maryam Salahinejad1, Tu C Le, David A Winkler

  • 1Faculty of Chemistry, Tarbiat Moallem University , Tehran 15719-14911, Iran.

Molecular Pharmaceutics
|May 31, 2013
PubMed
Summary
This summary is machine-generated.

Predicting drug aqueous solubility is crucial for pharmacokinetics. Surprisingly, including crystal lattice energy in models did not significantly improve solubility prediction accuracy for organic compounds.

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From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
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From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

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Controlling the Size, Shape and Stability of Supramolecular Polymers in Water
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Published on: August 2, 2012

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Published on: March 24, 2018

Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Aqueous solubility is a critical physical property for small molecule drugs, impacting drug delivery and pharmacokinetics.
  • Accurate prediction of aqueous solubility remains a significant challenge in drug development.
  • Crystal lattice interactions are hypothesized to influence solubility, suggesting their inclusion in predictive models could enhance accuracy.

Purpose of the Study:

  • To investigate whether incorporating crystal lattice energy and sublimation enthalpy as descriptors improves the accuracy of aqueous solubility prediction models.
  • To develop and evaluate predictive models for aqueous solubility using advanced statistical and machine learning techniques.
  • To assess the predictive performance on a large, diverse dataset of organic compounds.

Main Methods:

  • Utilized Multiple Linear Regression with an Expectation Maximization algorithm and a sparse prior (MLREM).
  • Employed a nonlinear Bayesian Regularized Artificial Neural Network with a Laplacian prior (BRANNLP).
  • Evaluated models using a large dataset of 4558 diverse organic compounds and validated on a separate test set and challenge compounds.

Main Results:

  • The BRANNLP method achieved the best statistical performance, with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for both training and test sets.
  • Contrary to expectations, the inclusion of descriptors representing crystal lattice interactions did not lead to a significant improvement in the predictive quality of the aqueous solubility models.
  • Both MLREM and BRANNLP models provided robust predictions, but the added complexity of lattice energy did not yield proportional gains in accuracy.

Conclusions:

  • While BRANNLP demonstrated superior performance in predicting aqueous solubility, the contribution of crystal lattice energy descriptors was unexpectedly marginal.
  • This suggests that other factors may dominate the aqueous solubility of organic molecules, or that current methods for quantifying lattice interactions require refinement.
  • Future research should explore alternative descriptors or modeling approaches to further enhance the accuracy of aqueous solubility predictions in drug discovery.