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相关概念视频

Chromatographic Methods: Terminology01:18

Chromatographic Methods: Terminology

2.5K
Chromatography is an analytical technique widely used in fields such as chemistry, biology, environmental science, and pharmaceuticals to separate the components of a mixture and identify substances between them. The process of chromatography is based on the interactions between two distinct phases: the stationary phase and the mobile phase. The stationary phase is fixed in place by a supporting material, while the mobile phase moves over it, carrying the solutes. As the mobile phase travels,...
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Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

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According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
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相关实验视频

Updated: Sep 15, 2025

Preparation of Human Tissues Embedded in Optimal Cutting Temperature Compound for Mass Spectrometry Analysis
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Preparation of Human Tissues Embedded in Optimal Cutting Temperature Compound for Mass Spectrometry Analysis

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基于机器学习的保留时间预测工具,用于常规LC-MS数据分析.

Sofiia A Dymura1, Oleksandr O Viniichuk1,2, Kostiantyn P Melnykov1,2

  • 1Enamine Ltd. (www.enamine.net), Winston Churchill Street 78, Kyiv 02094, Ukraine.

Journal of chemical information and modeling
|July 16, 2025
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概括
此摘要是机器生成的。

我们开发了一个图形神经网络模型,用于在液态染色体质谱法 (LC-MS) 中准确预测保留时间 (RT). 这增强了化学合成数据分析,并已集成到现有的工具包中.

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Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
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科学领域:

  • 分析化学 分析化学
  • 计算化学计算化学
  • 化学信息学 化学信息学

背景情况:

  • 液体染色学-质谱学 (LC-MS) 对于化学合成分析至关重要.
  • 准确的保留时间 (RT) 预测对于改善LC-MS数据处理至关重要.
  • 来自化学合成的大型内部数据集为模型开发提供了机会.

研究的目的:

  • 开发和评估一个新的RT预测模型用于LC-MS数据分析.
  • 利用内部实验数据和先进的神经网络架构.
  • 提高内部LC-MS工作流程的分析能力.

主要方法:

  • 使用GATv2Conv + DL架构开发一个图形神经网络 (NN) 模型.
  • 在化学合成实验的大型内部数据集上训练NN模型.
  • 使用METLIN SMRT数据集和120秒LC-MS方法对模型的评估.

主要成果:

  • 开发的RT预测模型实现了2.48秒的平均绝对误差 (MAE).
  • 超过95%的预测错误发生在RT ± 7.12秒到RT ± 9.58秒的间隔内.
  • 该模型成功地集成到内部LC-MS分析工具包中.

结论:

  • GATv2Conv + DL图形NN模型为LC-MS分析提供准确的RT预测.
  • 该模型显著提高了化学合成工作流程的预测和分析能力.
  • 一个20,000个数据点的子集公开发布,以支持社区研究和基准测试.