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相关概念视频

Principles Of Column Chromatography01:13

Principles Of Column Chromatography

7.2K
The chromatography technique was first invented in 1901 by Michael S. Tswett, a Russian botanist, to separate plant pigments using organic solvents. Further, in 1941, Archer John Porter Martin and R. L. M. Synge modified the technique by packing silica gel into a column. A mixture of amino acids was then separated on the packed column using chloroform and water mixture as the mobile phase. This was the first report on column chromatography. At present, column chromatography is a widely used...
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High-Performance Liquid Chromatography: Elution Process01:05

High-Performance Liquid Chromatography: Elution Process

690
In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
690
Types Of Column Chromatography01:29

Types Of Column Chromatography

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The stability and compatibility of column material with samples are crucial for efficient purification in chromatographic techniques. Various operating parameters such as pH, temperature, or solvent affect the packing of the column material, thereby determining the purification efficiency. The choice of column material also plays an essential role in deciding the operating parameters and can be modified based on the proteins that need to be purified.
Gel Filtration Chromatography
When the...
11.6K
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

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Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
492
Affinity Chromatography01:03

Affinity Chromatography

1.1K
Affinity chromatography is a powerful technique extensively utilized for separating and purifying specific biomolecules from complex mixtures. It capitalizes on the highly selective binding between an analyte and its counterpart, such as antibody-antigen interactions. The counterpart is immobilized on the stationary phase, forming an affinity column. The stationary phase typically consists of solid support, such as agarose or porous glass beads, immobilizing the affinity ligand. The mobile...
1.1K
High-Performance Liquid Chromatography: Introduction01:11

High-Performance Liquid Chromatography: Introduction

2.3K
High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
In HPLC, two phases play a critical role in the separation process:
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相关实验视频

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Digital Microfluidics for Automated Proteomic Processing
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Digital Microfluidics for Automated Proteomic Processing

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机器学习增强了蛋白质染色学中的过程设计.

Andrea Galeazzi1, Steven Sachio1, Elizabeth Edwards2

  • 1Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; Department of Chemical Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom.

Journal of chromatography. A
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概括

本研究引入了通过数字设计 (QbDD) 实现质量的机器学习方法,以有效地识别过程设计空间. 它使用合成数据的转移学习,减少了生物制药开发中广泛的湿实验室实验的需要.

关键词:
设计空间识别设计空间识别机器学习 机器学习蛋白质A是一种蛋白质.通过数字设计的质量.转移学习转移学习

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Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
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Author Spotlight: Optimizing Affinity Chromatography for His-Tagged FEN1 Protein
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相关实验视频

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Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
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科学领域:

  • 生物制药工艺开发 生物制药过程开发
  • 计算化学计算化学
  • 机器学习在药物发现中的作用

背景情况:

  • 数字设计的质量 (QbDD) 旨在通过减少对物理实验的依赖来加速生物制药开发.
  • 确定设计空间是QbDD的一个关键瓶,通常需要昂贵的高保真模型.
  • 目前用于设计空间识别的方法耗时且资源密集.

研究的目的:

  • 开发一种机器学习增强的方法,用于QbDD中高效的设计空间识别.
  • 利用合成数据和转移学习来克服传统方法的局限性,特别是在数据稀缺的情况下.
  • 为了证明这种方法在不同数据可用性场景 (高,中等,低) 中的适用性.

主要方法:

  • 利用转移学习框架与机械模型生成的合成数据集相结合.
  • 开发了一种人工神经网络 (ANN) 模型,用于分类可行的设计区域.
  • 在高,中等和低数据可用性 (HDA,MDA,LDA) 条件下评估了ANN模型的性能.

主要成果:

  • 数据驱动方法在HDA中表现出强的表现.
  • 转移学习显著提高了MDA中的模型准确性,对LDA中的性能至关重要.
  • 机器学习方法有效地确定了可行的设计区域,证明了它的实用性.

结论:

  • 机器学习,特别是转移学习,为QbDD中的设计空间识别提供了强大而高效的解决方案.
  • 这种方法可以显著降低与早期生物制药工艺设计相关的成本和时间.
  • 开发的方法有可能简化QbDD的实施并加速药物开发时间表.