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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

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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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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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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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Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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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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Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

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Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
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相关实验视频

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Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
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高保真与低保真实验数据在机器学习模型性能中的作用,用于预测聚合物可溶性.

Mona Amrihesari1, Manali Banerjee2, Raul Olmedo1

  • 1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

Macromolecular rapid communications
|July 28, 2025
PubMed
概括

高准确度的实验数据显著改善了用于预测聚合物可溶性的机器学习模型. 量化测量优于视觉检查,用于训练聚合物科学中准确的AI工具.

关键词:
数据集数据集数据集机器学习是机器学习.预测 预测 预测 预测溶解度 溶解度 溶解度 溶解度

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Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry
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科学领域:

  • 聚合物科学 聚合物科学
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 聚合物与溶剂的兼容性对于开发新材料和配方至关重要.
  • 机器学习 (ML) 和人工智能 (AI) 显示出预测聚合物可溶性的前景.
  • 模型的性能取决于培训数据的质量和性质.

研究的目的:

  • 评估实验数据的真实性如何影响ML模型对聚合物-溶剂兼容性的性能.
  • 为了比较训练在高保真度 (基于度) 和低保真度 (视觉检查) 数据集上的ML分类器.
  • 确定影响基于ML的可溶性预测准确性的关键因素.

主要方法:

  • 创建了两个数据集:来自度测量的高保证度 (Crystal16) 和来自视觉检查的低保证度.
  • 聚合物使用一次热编码进行编码,溶剂使用摩根指纹进行编码.
  • 使用XGBoost分类器预测可溶性标签 (可溶性,不可溶性,部分可溶性).

主要成果:

  • 在高可靠性数据上训练的模型表现出卓越的性能,更好地捕捉部分可溶性行为.
  • 与主观视觉检查相比,定量测量导致了更清晰的阶级区别.
  • 包括温度作为一个特征,提高了低保真数据集的预测准确度.

结论:

  • 实验数据的严谨性对于开发聚合物科学中可概括的ML工具至关重要.
  • 高准确度的定量数据来源对于可靠的AI驱动的聚合物可溶性预测至关重要.
  • 在利用文献衍生数据集时,考虑温度等实验参数至关重要.