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相关概念视频

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

737
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
737

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相关实验视频

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Screening for Endocrine Activity in Water Using Commercially-available In Vitro Transactivation Bioassays
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评估基于结构的活性在一个高通量测试中的类固醇生物合成.

M J Foster1,2, G Patlewicz1, I Shah1

  • 1Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.

Computational toxicology (Amsterdam, Netherlands)
|October 16, 2023
PubMed
概括

这项研究开发了计算模型来预测化学物质如何使用现有测试数据影响类固醇激素生产. 这些方法有效地在数千种未经测试的化学物质中识别出潜在的内分泌干扰物,以便进一步选.

关键词:
类固醇的产生.化学类型 化学类型在片选中阅读横向的阅读.结构 - 活动关系结构 - 活动关系

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科学领域:

  • 毒理学和药理学的毒理学和药理学.
  • 计算化学是一种计算化学.
  • 内分泌学 在内分泌学.

背景情况:

  • 对于许多化学物质,可获得类固醇激素生物合成的高通量查 (HTS) 数据.
  • 目前用于评估化学物质对类固醇生成的影响的方法因数据的可用性而受到限制.
  • 需要预测模型来优先考虑未经测试的化学物质HTS.

研究的目的:

  • 开发和验证用于预测对类固醇激素生物合成化学效应的in silico模型.
  • 确定与内分泌干扰相关的结构特征和物理化学特性.
  • 优先考虑大量未经测试的化学品进行实验评估.

主要方法:

  • 利用现有的高通量人类上腺皮质癌 (HT-H295R) 试验数据.
  • 构建的定量结构-活动关系 (QSAR) 和机器学习模型.
  • 采用了ToxPrint化学型,物理化学性质,随机森林 (RF) 和最近邻居 (NN) 算法.

主要成果:

  • 个体化学型显示高特异性,但对雌激素和雄激素合成调节剂的敏感性较低.
  • 最好的射频模型实现了71%的平衡精度 (BA) 预测maxmMd结果.
  • 最接近的邻居模型表现出优异的性能,BA为85% (化学型) 和81% (摩根指纹).
  • 在6302种未经测试的化学物质中,有1241种被确定为假定的雌激素和雄激素调节剂.

结论:

  • 在 silico 方法,特别是 NN 模型,可以有效地预测对类固醇生物合成的化学影响.
  • 这些计算方法有效地优先选择成千上万种化学品进行选,有助于识别内分泌干扰物.
  • 这项研究为评估缺乏数据的化学品的内分泌干扰潜力提供了有价值的工具.