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相关概念视频

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Inductive Effects on Chemical Shift: Overview01:27

Inductive Effects on Chemical Shift: Overview

The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...

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F-CPI:一种多模式深度学习方法,用于预测由替代引起的复合生物活性变化.

Qian Zhang1, Wenhai Yin1, Xinyao Chen2,3

  • 1School of Computer Science and Technology, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, East China Normal University, Shanghai 200241, China.

Journal of medicinal chemistry
|December 20, 2024
PubMed
概括

这项研究介绍了F-CPI,这是一种深度学习模型,用于预测替代如何影响药物化合物与蛋白质相互作用. 通过准确预测生物活性变化,F-CPI改善了药物发现,超过了现有的方法.

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科学领域:

  • 药用化学 医学化学
  • 计算化学计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 替代是药物发现的一个关键策略.
  • 预测由替代产生的生物活性变化及其对化合物-蛋白相互作用 (CPI) 的影响仍然具有挑战性.
  • 对对消费者消费指数的影响缺乏准确的预测模型.

研究的目的:

  • 开发一个预测模型,用于由替代引起的生物活性变化.
  • 研究替代对化合物-蛋白相互作用 (CPI) 的影响.
  • 通过准确的预测,提高药物发现和设计的效率.

主要方法:

  • 构建一个大型数据集,包括111,168对替代和非替代化合物.
  • 开发一种名为F-CPI的多式联络深度学习模型.
  • 对F-CPI与传统机器学习和现有的CPI模型进行比较分析.

主要成果:

  • 与GraphDTA相比,F-CPI模型在准确性 (∼90%),精度 (∼79%) 和回忆 (∼45%) 方面表现出卓越的性能.
  • F-CPI的应用导致SARS-CoV-2 3CLpro抑制剂的生物活性增加了100倍以上.
  • 优化的化合物显著改善了抑制度50 (IC50) 值 (0.23μM vs 28.19μM).

结论:

  • 多模式深度学习模型F-CPI是预测替代对生物活性影响的有效工具.
  • F-CPI促进了药物发现中的结构优化,导致显著的生物活性增强.
  • 该模型为推进药物设计和开发过程提供了一种有价值的方法.