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相关概念视频

Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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相关实验视频

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Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports
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基于交叉列密度功能理论的定量结构-保留关系模型开发,由机器学习驱动.

Sargol Mazraedoost1, Petar Žuvela1, Szymon Ulenberg2

  • 1Intelligent Systems Laboratory, Department of Chemical Engineering, Pukyong National University, Busan, 48513, Republic of Korea.

Analytical and bioanalytical chemistry
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概括
此摘要是机器生成的。

这项研究开发了使用机器学习和量子力学描述器的跨列定量结构-保留关系 (QSRR) 模型,以预测高性能液态染色学 (HPLC) 中的保留时间. 梯度增强模型在不同的列和条件中取得了出色的预测性能.

关键词:
化学信息学 化学信息学密度函数理论 (DFT) 是一种密度函数理论.机器学习 (ML) 是指机器学习.定量结构与保留关系 (QSRR)保留时间预测预测.逆相高性能液态染色学 (RP-HPLC) 是一种

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科学领域:

  • 分析化学 分析化学
  • 计算化学计算化学
  • 染色体学 染色体学 是一种染色学.

背景情况:

  • 定量结构-保留关系 (QSRR) 建模使用分子描述器预测分析物的保留时间.
  • 现有的QSRR模型通常是专用列的,这限制了它们在不同的高性能液态染色学 (HPLC) 系统中的应用.
  • 开发适用于各种列和条件的通用QSRR模型是一个重大挑战.

研究的目的:

  • 开发和评估基于机器学习 (ML) 的QSRR模型,能够预测跨多个反相HPLC列的保留时间.
  • 研究量子力学 (QM) 描述符与跨列QSRR建模的实验参数相结合的实用性.
  • 为了比较不同ML算法的性能,包括PLS,RR,RF和GB,用于预测色谱保留时间.

主要方法:

  • 使用密度函数理论 (DFT) 计算了15种芳香分析物的量子力学 (QM) 描述器.
  • 使用四个ML算法开发QSRR模型:部分最小平方 (PLS),回归 (RR),随机森林 (RF) 和梯度增强 (GB).
  • 作为额外的描述符,包含列特征 (粒子大小,孔径大小) 和实验条件 (温度,梯度时间).

主要成果:

  • 梯度增强 (GB) QSRR模型实现了最高的预测性能,试验组的Q2为0.989和RMSEP为0.749分钟.
  • 关键的有影响力的描述因素包括溶解能量 (SE),HOMO-LUMO能量差距 (∆E HOMO-LUMO),总二极极矩 (Mtot) 和全球硬度 (η),突出了静电相互作用和疏水性的作用.
  • 组合方法 (GB,RF) 在捕捉不同实验设置中保留时间的局部变化方面表现出卓越的能力.

结论:

  • 机器学习算法,特别是像梯度增强这样的组合方法,对于开发跨列QSRR模型非常有效.
  • 量子力学描述器在预测保留时间方面做出了重大贡献,强调了分子电子特性和疏水性的重要性.
  • 这项研究表明了通用QSRR模型的潜力,可以优化染色体分析,减少对专列特定方法开发的需求.