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相关概念视频

Potentiometric Titration: Overview01:31

Potentiometric Titration: Overview

1.2K
Potentiometric titration is a quantitative analytical technique that determines the concentration of an analyte by measuring the potential difference between the two electrodes in the solution. The endpoint of a potentiometric titration is the point at which there is a significant change in the potential difference. It occurs when the stoichiometric reaction between the analyte and the titrant is complete. The endpoint is usually determined graphically by plotting the measured potential...
1.2K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

318
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
318
Ladder Diagrams: Redox Equilibria01:30

Ladder Diagrams: Redox Equilibria

454
Ladder diagrams are useful tools for understanding redox equilibrium reactions, especially the effects of concentration changes on the electrochemical potential of the reaction. The vertical axis in the redox ladder diagrams represents the electrochemical potential, E. The area of predominance is demarcated using the Nernst equation.
Consider the Fe3+/Fe2+ half-reaction, which has a standard-state potential of +0.771 V. At potentials more positive than +0.771 V, Fe3+ predominates, whereas Fe2+...
454
Redox Titration: Other Oxidizing and Reducing Agents01:26

Redox Titration: Other Oxidizing and Reducing Agents

280
Besides iodine, other oxidizing or reducing agents can serve as titrants in redox titrations. Common oxidizing titrants include KMnO4, cerium(IV), and K2Cr2O7. The choice of oxidizing titrants depends on factors like stability, cost, analyte strength, and reaction rate between the analyte and titrant. KMnO4 is a strong oxidizing titrant that reduces from Mn(VII) to Mn(II) in a highly acidic solution, simultaneously oxidizing the analyte to a higher oxidation state. In this case, KMnO4 acts as a...
280
Complexometric Titration: Ligands00:43

Complexometric Titration: Ligands

948
Different monodentate and polydentate ligands are used as complexing agents in complexometric titration reactions. The formation of complexes by mono- and bidentate ligands involves two or more intermediate steps, limiting their use as complexing agents. In comparison, polydentate ligands can form complexes with metal ions in a single-step process, facilitating sharper end points. This means polydentate ligands, such as amino carboxylic acid derivatives, are most commonly employed in...
948
Valence Bond Theory02:42

Valence Bond Theory

8.5K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
8.5K

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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基于机器学习的PtIV复合体的减少潜力的预测.

V Vigna1, T F G G Cova2, S C C Nunes2

  • 1PROMOCS Laboratory, Department of Chemistry and Chemical Technologies, University of Calabria, Arcavacata di Rende87036,Italy.

Journal of chemical information and modeling
|April 29, 2024
PubMed
概括

机器学习准确地预测的复杂降解潜力,这对于开发释放活性物种的惰性前药来说至关重要. 这加速了新型癌症治疗方法的设计.

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科学领域:

  • 计算化学计算化学
  • 药用化学 医学化学
  • 机器学习在药物发现中的作用

背景情况:

  • (IV) 复合物比 (II) 药物具有优势,作为惰性前药物.
  • 它们的激活依赖于减少到活跃的 (II) 种,而减少潜力是关键因素.
  • 了解和预测降低潜力对于合理的药物设计至关重要.

研究的目的:

  • 开发一种机器学习 (ML) 模型,用于预测 (IV) 复合物的降解潜力.
  • 确定影响这些电化学性质的关键分子描述因素.
  • 为了促进新(IV) 预制药的合理设计,以量身定制的制药应用.

主要方法:

  • 利用了对 (IV) 复合物的实验确定还原潜力的数据集.
  • 采用各种机器学习算法和功能工程技术.
  • 包含了初始计算和各种分子描述符 (宪法,拓,电子).

主要成果:

  • 实现了降解潜力的高预测精度 (R2 = 0.92,RMSE = 0.13 V).
  • 确定了二维原子对描述符和最小的未被占用分子轨道能量作为重要的特征.
  • 证明了一组精选的20个描述符可以有效地通过减少潜力来区分复杂物.

结论:

  • ML方法提供了一个快速有效的工具,用于预测的复杂降解潜力.
  • 这种方法在合理设计和选新白金(IV) 前药候选物方面有显著的帮助.
  • 这些发现为医学中的电化学应用提供了对结构属性关系的宝贵见解.