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相关概念视频

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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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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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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评估可扩展的监督学习,用于按需合成的化学库.

Moayad Alnammi1,2,3, Shengchao Liu1,2, Spencer S Ericksen4

  • 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.

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|August 25, 2023
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概括

机器学习驱动的虚拟选成功地在大型化学库中识别了细菌PriA-SSB目标的活性化合物. 这种方法显著优于传统方法,加速了药物发现.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习是机器学习.

背景情况:

  • 传统的药物发现是缓慢而昂贵的,高通量选只检查一个小的化学空间.
  • 机器学习 (ML) 为大型化学库提供了强大的虚拟选 (VS) 功能,但缺乏广泛的实验验证.

研究的目的:

  • 预期评估基于连接体的虚拟查,使用ML识别针对细菌蛋白质-蛋白质相互作用的活性化合物PriA-SSB.
  • 为了比较不同ML模型的性能,并验证大型商业和定制化学图书馆的发现.

主要方法:

  • 交叉验证用于比较监督学习模型,选择一个随机森林 (RF) 分类器作为最佳.
  • 射频模型选了来自Aldrich Market Select的800多万种化合物和来自Enamine REAL数据库的10亿种化合物.
  • 实验验证是在两个图书馆中选择的化合物上进行的.

主要成果:

  • 射频模型显著优于结构相似性基线,在701个选定的化合物中,48%的化合物显示对PriA-SSB的活性.
  • 从Enamine REAL测试68个不同的顶级预测结果,得到31个结果 (46%),其中一个是1.3μM IC50.
  • ML方法在识别新型活性化合物方面展示了可扩展性和有效性.

结论:

  • 基于联体的虚拟选由ML,特别是RF驱动,是从庞大的化学库中发现活性小分子的高效策略.
  • 这种经过验证的ML方法加速了候选药物的识别,克服了传统选方法的局限性.
  • 该研究强调了ML驱动的VS在现实世界药物发现计划中的实用实用性.