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相关概念视频

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism

403
Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
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Entropy and Solvation02:05

Entropy and Solvation

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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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...
1.9K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

3.1K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
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Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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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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Updated: Sep 10, 2025

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
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基于数据的框架可对各种聚合物具有可靠的可溶性参数进行预测

Raouf Hassan1, Mohammad Reza Kazemi2

  • 1Civil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia.

Scientific reports
|August 24, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型使用各种输入特征准确预测聚合物可溶性参数. CatBoost,人工神经网络 (ANN) 和卷积神经网络 (CNN) 在预测聚合物溶解性方面表现出卓越的表现.

关键词:
基于数据的模型机器学习聚合物SHAP分析可溶性

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科学领域:

  • 聚合物科学
  • 计算化学
  • 材料科学

背景情况:

  • 准确预测聚合物可溶性参数对于材料选择和加工至关重要.
  • 了解聚合物特性与可溶性之间的复杂关系对于开发新材料至关重要.
  • 目前用于确定溶解度参数的方法可能耗时且资源密集.

研究的目的:

  • 开发和评估用于准确预测聚合物可溶性参数的机器学习 (ML) 模型.
  • 确定影响聚合物可溶性的关键分子描述因素.
  • 提高聚合物科学中的ML模型的可解释性和预测可靠性.

主要方法:

  • 使用了1,799个聚合物溶解度数据点的数据集,并通过蒙特卡洛异常检测进行了预处理.
  • 训练并比较多个ML算法,包括线性回归,SVM,随机森林,梯度增强机,ANN和CNN.
  • 使用R平方,RMSE,MRD%,交叉图,偏差图和SHAP分析评估模型性能.

主要成果:

  • 在预测聚合物可溶性参数方面,CatBoost,ANN和CNN模型取得了卓越的准确性.
  • 敏感性分析证实所有输入特征都影响了溶解度参数.
  • SHAP分析发现介电常数是聚合物可溶性的最重要的预测指标.

结论:

  • 机器学习模型,特别是CatBoost,ANN和CNN,为预测聚合物可溶性参数提供了高效和准确的方法.
  • 关键的分子描述物,特别是介电常数,在确定聚合物溶解度方面发挥着至关重要的作用.
  • 开发的模型为结构与性质关系提供了宝贵的见解,增强了聚合物科学的科学理解和预测能力.