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相关概念视频

Drug Concentration Versus Time Correlation01:15

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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
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Size-Exclusion Chromatography01:08

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In size-exclusion chromatography (SEC), also known as molecular-exclusion or gel-permeation chromatography, molecules are separated based on their sizes. This technique is important for separating large molecules such as polymers and biomolecules. The two classes of micron-sized stationary phases encountered in SEC are silica particles and cross-linked polymer resin beads. Both materials are porous, but their pore sizes vary significantly.
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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

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At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
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When drugs are administered extravascularly, a comprehensive evaluation through noncompartmental analysis becomes imperative. This analytical approach considers various parameters that play a crucial role in understanding the pharmacokinetics of these drugs.
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Noncompartmental Analysis: Mean Residence Time01:05

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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.
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Protons bonded to heteroatoms such as nitrogen and oxygen exhibit a range of chemical shift values. This is due to the varying degree of hydrogen bonding between the proton and the heteroatom in other molecules. The extent of hydrogen bonding affects the electron density around the proton, thereby giving different chemical shift values for the protons in the proton NMR spectrum.
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Updated: Jul 12, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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深度图形卷积网络用于小分子保留时间预测.

Qiyue Kang1, Pengfei Fang2, Shuai Zhang1

  • 1School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China.

Journal of chromatography. A
|October 21, 2023
PubMed
概括
此摘要是机器生成的。

深度图形神经网络 (GNN) 显著改善了液态染色体质谱法 (LCMS) 中的保留时间 (RT) 预测. 更深层次的GNN,增强了剩余连接和边缘信息,实现分子识别的最先进的准确性.

关键词:
图表神经网络的神经网络保持时间预测预测.转移学习转移学习

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科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 在分析化学中的机器学习.

背景情况:

  • 保留时间 (RT) 对于液态染色体质谱法 (LCMS) 中的分子鉴定至关重要.
  • 图形神经网络 (GNN) 对RT预测有希望,但其深度尚未优化.
  • 准确的RT预测有助于过具有相似光谱但不同RT的分子候选物.

研究的目的:

  • 调查GNN深度对RT预测准确性的影响.
  • 开发一个深入的GNN模型,以在LCMS中增强RT预测.
  • 评估模型在各种染色学条件下的性能及其在分子结构识别中的实用性.

主要方法:

  • 利用了带有剩余连接和边缘信息的深度图形卷积网络 (GCN).
  • 研究了增加GNN深度到16层的效果.
  • 在七个不同的LCMS数据集上微调DeepGCN-RT模型.
  • 评估模型对分子结构识别准确性的影响.

主要成果:

  • 一个具有残余连接的16层GNN显著改善了RT预测.
  • 在DeepGCN-RT模型中,在SMRT测试组中,平均绝对百分比误差 (MAPE) 为3.3%,平均绝对误差 (MAE) 为26.55秒.
  • 与以前的方法相比,微调将七个数据集的平均MAE降低了30%.
  • 在分子结构识别中,DeepGCN-RT通过将候选结构减少30%,提高了11%的top-1精度.

结论:

  • 更深层次的GNN,特别是剩余连接和边缘信息,提高了LCMS中RT预测的准确性.
  • 开发的DeepGCN-RT模型代表了与现有的RT预测和分子识别方法相比的重大进步.
  • DeepGCN-RT在各种染色学条件中展示了广泛的适用性,并有效地协助阐明复杂的分子结构.