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相关概念视频

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相关实验视频

Updated: May 17, 2025

Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay
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发展性毒性:人工智能驱动的评估.

Tong Wang1, Xuelian Jia1, Lauren M Aleksunes2

  • 1Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ, USA.

Trends in pharmacological sciences
|May 15, 2025
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概括

人工智能 (AI) 可以分析大数据,预测产前药物暴露的发育毒性. 这种方法有助于识别孕妇和胎儿的药物风险,改善安全评估.

关键词:
人工智能的人工智能是人工智能.计算毒理学计算毒理学发展性毒性 发展性毒性可以解释的建模.多式联运数据是多式联运数据.

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科学领域:

  • 毒理学和药理学 毒理学和药理学
  • 计算生物学和生物信息学
  • 药物开发和监管科学 药物开发和监管科学

背景情况:

  • 监管机构要求对产前药物暴露进行全面的毒性测试.
  • 评估发育性毒性,包括药物诱导的孕妇和胎儿的不良影响,存在重大挑战.
  • 定义发育性毒性终点和分析大型公共数据集仍然是复杂的.

研究的目的:

  • 提供大数据资源和数据驱动模型的概述,用于预测发育性毒性终点.
  • 为突出分析复杂毒性数据的新兴可解释的AI模型.
  • 提出一个框架,用人工智能评估化学诱导的发育毒性.

主要方法:

  • 对与发育毒性相关的主要大数据资源的审查.
  • 分析数据驱动模型,包括人工智能 (AI) 方法,用于毒性预测.
  • 专注于可解释的AI模型,整合多式联网数据和领域知识.

主要成果:

  • 人工智能方法对于分析高维数据至关重要,以揭示化学物质暴露和发育风险之间的关系.
  • 新兴的可解释的人工智能模型可以揭示复杂的发育毒性终点背后的有毒机制.
  • 一个利用多个可解释模型的框架可以全面评估化学诱导的发育毒性.

结论:

  • 人工智能为分析发育毒性评估中的复杂数据集提供了强大的工具.
  • 可解释的人工智能模型是理解药物诱导发育毒性的机制的关键.
  • 一个整合可解释AI的框架可以增强产前药物安全性的评估.