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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

1.9K
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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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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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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Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
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多様なポリマーの溶解性パラメータを堅牢に予測するためのデータ主導のフレームワーク

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
まとめ
この要約は機械生成です。

機械学習モデルは,様々な入力機能を使用して,ポリマーの溶解性パラメータを正確に予測します. CatBoost,人工ニューラルネットワーク (ANN),およびコンボリューションニューラルネットワーク (CNN) は,ポリマー溶解性を予測する上で優れたパフォーマンスを示しました.

キーワード:
データ駆動モデル機械学習ポリマーSHAP分析について溶解性

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Synthesis of Terpolymers at Mild Temperatures Using Dynamic Sulfur Bonds in PolyS-Divinylbenzene
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A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy
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科学分野:

  • ポリマー科学
  • コンピュータ化学
  • 材料科学

背景:

  • ポリマーの溶解性パラメータの正確な予測は,材料の選択と処理に不可欠です.
  • ポリマーの性質と溶解性の複雑な関係を理解することは,新しい材料の開発に不可欠です.
  • 溶解性パラメータを決定するための既存の方法は,時間がかかり,資源が密集している可能性があります.

研究 の 目的:

  • ポリマーの溶解性パラメータの正確な予測のための機械学習 (ML) モデルを開発し評価する.
  • ポリマーの溶解性に影響する重要な分子記述者を特定する.
  • ポリマー科学におけるMLモデルの解釈性と予測信頼性を高める.

主な方法:

  • ポリマー溶解度データポイント1799のデータセットを使用し,モンテカルロ偏差値検出で事前処理しました.
  • 線形回帰,SVM,ランダムフォレスト,グラデントブースティングマシン,ANN,CNNを含む複数のMLアルゴリズムをトレーニングし,比較しました.
  • R-squared,RMSE,MRD%,クロスグラフ,偏差グラフ,SHAP分析を用いてモデルの性能を評価した.

主要な成果:

  • CatBoost,ANN,CNNモデルは,ポリマーの溶解性パラメータを予測する上で優れた精度を達成しました.
  • 感度分析は,すべての入力特性が溶解性パラメータに影響を与えたことを確認した.
  • SHAP分析では,ポリマーの溶解性の最も重要な予測値として介電常数が特定されました.

結論:

  • MLモデル,特にCatBoost,ANN,CNNは,ポリマーの溶解性パラメータを予測するための効率的で正確なアプローチを提供します.
  • 重要な分子記述子,特に介電定数は,ポリマーの溶解性を決定する上で重要な役割を果たします.
  • 開発されたモデルは,構造と性質の関係に関する貴重な洞察を提供し,ポリマー科学における科学的理解と予測能力を高めています.