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相关概念视频

Solubility Equilibria: Overview01:09

Solubility Equilibria: Overview

677
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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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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Chemical and Solubility Equilibria02:21

Chemical and Solubility Equilibria

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The free energy change associated with dissolving a solute in a liter of solvent is called the free energy of a solution, ΔGsolution. The overall ΔGsolution is expressed as the balance of ΔGinteraction against the always-favorable free-energy of mixing, ΔGmixing. Solution formation is favorable if  ΔGsolution is less than zero, whereas it is unfavorable if ΔGsolution is greater than zero. In short, for a solution to form and complete dissolution to take place,...
4.1K
Solution Formation02:16

Solution Formation

31.6K
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 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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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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Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
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SolPredictor:使用剩余门式图形神经网络预测溶解度

Waqar Ahmad1, Hilal Tayara2, HyunJoo Shim3

  • 1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.

International journal of molecular sciences
|January 23, 2024
PubMed
概括

使用余图神经网络 (RGNN) 的新型计算模型SolPredictor准确预测分子溶解度. 这加快了药物发现,减少了大量实验室工作的需要,节省了时间和成本.

关键词:
接收人 接收人人工智能的人工智能是人工智能.发现药物的发现.图表神经网络的神经网络分子溶解度 分子溶解度这是一个回归回归的回归.剩余封闭图形神经网络的神经网络简化分子输入线路输入系统

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 计算方法通过预测化合物特性来加速药物发现.
  • 机器和深度学习模型对于in silico可溶性预测是有效的.
  • 准确的可溶性预测有助于配方,优化和药物动力学评估,降低成本和时间表.

研究的目的:

  • 开发一个先进的计算模型,SolPredictor,用于精确的分子溶解度预测.
  • 为了利用残余图神经网络卷积 (RGNN) 来捕获复杂的分子依赖关系.

主要方法:

  • 使用简化分子输入线路输入系统 (SMILES) 表示.
  • 编译了两个大型数据集,并采用了十倍的交叉验证.
  • 集成的RGNN设计用于远程依赖性捕获和残余连接以保护特征.

主要成果:

  • 取得了0.79±0.02的皮尔森相关系数 (R2) 和1.03±0.04的根平均平方误差 (RMSE).
  • 在五个独立数据集上验证了模型.
  • 进行错误,超参数优化和可解释性分析,以确定关键的预测分子特征.

结论:

  • SolPredictor在in silico可溶性预测方面表现出高精度.
  • 基于RGNN的方法有效地捕获了精确预测的基本分子特征.
  • 这种模式为加速药物开发和降低相关成本提供了巨大的潜力.