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开发基于GNN的AI模型,使用包装方法预测线粒体毒性.

Yoshinobu Igarashi1, Ryosuke Kojima2, Shigeyuki Matsumoto2

  • 1Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health and Nutrition.

The Journal of toxicological sciences
|March 3, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种人工智能模型,使用化学结构预测线粒体毒性,识别关键子结构. 该工具通过改善线粒体安全性潜在药物候选人的选来帮助早期药物发现.

关键词:
包装方式 包装方法深度学习是一种深度学习.可解释的人工智能图形神经网络是一个神经网络.线粒体中毒性 线粒体中毒性

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科学领域:

  • 生物化学 生物化学
  • 毒理学 毒理学 毒理学
  • 计算化学的计算化学

背景情况:

  • 线粒体毒性是药物开发中的关键因素,与肝毒性等各种毒性有关.
  • 当前的预测模型提供了二元结果,而不确定有助于结构元素.
  • 早期查线粒体毒性至关重要,以防止晚期失败.

研究的目的:

  • 开发一种人工智能 (AI) 模型,用于预测化学结构的线粒体毒性.
  • 为了能够可视化结构性警报,有助于预测毒性.
  • 通过提供详细的毒性见解来增强早期药物发现.

主要方法:

  • 利用kMoL软件库与图形神经网络方法进行化学结构学习.
  • 采用集成梯度方法来可视化与积极毒性预测相关的亚结构.
  • 使用包装方法解决了数据集不平衡,以提高模型性能.

主要成果:

  • 开发了一个具有高预测性能的AI模型,获得0.839.1的F1得分.
  • 成功可视化了有助于线粒体毒性预测的特定基层结构.
  • 证明了该模型在识别潜在的线粒体毒素方面的能力.

结论:

  • 开发的AI模型根据化学结构准确预测线粒体毒性.
  • 该模型的可视化功能有助于理解毒性的结构基础.
  • 这种工具可以为线粒体安全性候选药物的早期查做出重大贡献.