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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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分析代谢途径的图形卷积网络 (GCN) 改善了基因毒性预测. 这种方法的性能优于传统的化学结构分析,用于跨读 (RAx) 评估.

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科学领域:

  • 计算毒理学计算毒理学
  • 化学信息学 化学信息学
  • 预测建模预测建模

背景情况:

  • 在化学安全评估中,代谢相似性对于交叉阅读 (RAx) 至关重要.
  • 目前用于表征RAx代谢的方法仍在开发中.
  • 代谢相似性涉及代谢树,模拟代谢物和转化途径.

研究的目的:

  • 将代谢图表表示与结构相似性进行比较,以预测遗传毒性.
  • 评估图形卷积网络 (GCNs) 在编码RAx.代谢信息中的性能.
  • 用代谢途径数据确定预测基因毒性的最有效方法.

主要方法:

  • 使用TIssue代谢模拟器 (TIMES) 和生物转换器预测异生菌代谢.
  • 将代谢途径转换为图形并训练GCN产生化学嵌入物.
  • 使用GCN嵌入与化学指纹 (摩根,MACCS) 的GenRA,RF,LR和MLP的比较分类性能.

主要成果:

  • 使用TIMES代谢预测和MACCS指纹的物流回归 (LR) 的GCN嵌入,实现了最高的AUC (0.807).
  • 这种基于GCN的方法在MACCS指纹的GenRA和LR的表现分别超过了14.47%和5.49%.
  • 预测代谢途径的GCN嵌入显示出高于母化学结构特征的性能.

结论:

  • 预测代谢途径的GCN嵌入对于基因毒性预测非常有效.
  • 这种方法提供了一种系统的方式来编码代谢信息,以改善RAx.中的模拟识别.
  • 这些发现支持使用GCN来增强预测性毒理学和化学安全评估.