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相关概念视频

Molecular Models02:00

Molecular Models

38.1K
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.
38.1K
¹H NMR: Pople Notation01:09

¹H NMR: Pople Notation

1.7K
The Pople nomenclature system classifies spin systems based on the difference between their chemical shifts. Coupled spins are denoted by capital letters with subscripts indicating the number of equivalent nuclei. When the coupled nuclei have well-separated chemical shifts, they are assigned letters that are far apart in the alphabet, such as A and X. When the difference in chemical shifts is small, coupled nuclei are named using adjacent letters of the alphabet (AB, MN, or XY).
A proton...
1.7K
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

1.3K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
1.3K
Molecular Shapes01:18

Molecular Shapes

56.8K
Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...
56.8K
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.8K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
2.8K
Molecules and Compounds02:38

Molecules and Compounds

55.1K
Atoms and Molecules
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相关实验视频

Updated: Jun 14, 2025

Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy
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Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy

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在分子对上使用主动学习找到最强效的化合物.

Zachary Fralish1, Daniel Reker1

  • 1Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.

Beilstein journal of organic chemistry
|September 3, 2024
PubMed
概括

ActiveDelta通过配对化合物来增强分子优化的积极学习,以提高功效和支架的多样性,特别是在有限的数据的情况下. 这种适应性方法通过更快地识别更好的候选药物来加速药物发现.

科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 积极学习加速了分子优化,但在早期阶段与有限的数据作斗争,可能产生具有低架构多样性的类似物.
  • 剥削性的积极学习可以导致模型过度适应初始数据,限制对新化学空间的探索.

研究的目的:

  • 引入ActiveDelta,一种适应性主动学习方法,使用配对的分子表示来预测改进和指导数据采集.
  • 评估ActiveDelta在提高已识别的分子抑制剂的功效和化学多样性的有效性.

主要方法:

  • 应用了基于图形的深度 (Chemprop) 和基于树的 (XGBoost) 模型进行主动学习.
  • 在99K的基准测试数据集上评估了性能,将ActiveDelta与标准的积极学习方法 (Chemprop,XGBoost,Random Forest) 进行比较.
  • 化学多样性使用Murcko支架进行评估.

主要成果:

  • 在识别更强大的抑制剂方面,ActiveDelta的实施显著超过了标准的积极学习.
  • 该方法成功识别了基于Murcko支架的更大的化学多样性分子抑制剂.
  • 用ActiveDelta选择的数据训练的深度学习模型 (Chemprop) 在模拟的时间分割测试集中显示出更好的准确性.
关键词:
积极学习是积极学习.药物设计 药物设计机器学习是机器学习.分子优化分子优化强度预测 强度预测

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相关实验视频

Last Updated: Jun 14, 2025

Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy
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Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy

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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

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Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

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结论:

  • ActiveDelta代表了积极学习策略的重大进步,特别是在低数据场景中.
  • 像ActiveDelta这样的分子配对方法可以加速识别强效和多样化的候选药物.
  • 这种方法具有很大的潜力,可以改善针对关键药物目标的击中识别.