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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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通过非重叠的掩盖来进行互补的多模式分子自我监督学习,以预测属性.

Ao Shen1,2, Mingzhi Yuan1,2, Yingfan Ma1,2

  • 1Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 131 Dong'an Road, 200032, Shanghai, China.

Briefings in bioinformatics
|May 27, 2024
PubMed
概括

这项研究引入了一种新的多模式自我监督的学习框架,用于分子表示. 通过整合SMILES和图形数据,它提高了化学性质预测和虚拟选任务的性能.

关键词:
分子性质预测分子性质预测分子表示的分子表征.多模式自主监督学习学习

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习 机器学习
  • 化学信息学 化学信息学

背景情况:

  • 标记的分子数据很少用于诸如属性预测等任务.
  • 现有的方法往往忽略了来自分子SMILES和图形表示的综合信息.
  • 有效的分子表示学习对于推动药物发现和化学研究至关重要.

研究的目的:

  • 开发一个多模式的自我监督的学习框架,用于分子表示.
  • 利用SMILES字符串和分子图表中的互补信息.
  • 通过使用统一的方法,提高下游分子任务的性能.

主要方法:

  • 一个统一的基于变压器的骨干网络处理了令牌化的SMILES和图形数据.
  • 面具重建策略用于预训练.
  • 一个专门的非重叠的掩盖策略促进了跨模式的互动.

主要成果:

  • 拟议的框架实现了对分子性质预测任务的最先进性能.
  • 废弃性研究证实了多模式方法的有效性.
  • 专业化掩盖策略显著有助于改善模型性能.

结论:

  • 多模式自主监督学习框架有效地捕获了丰富的分子信息.
  • 整合SMILES和图形数据可以增强分子表示学习能力.
  • 这种方法为未来的分子机器学习研究提供了有希望的方向.