化学空间网络通过图形嵌入增强毒性识别
F Mastrolorito1, N Gambacorta2, F Ciriaco3
1Dipartimento di Farmacia-Scienze del Farmaco, Universit̀a degli studi di Bari Aldo Moro, Bari 70125, Italy.
Journal of chemical information and modeling
|February 6, 2025
概括
化学空间网络 (CSN) 有效地识别化学毒性模式. 将CSN结构与图形神经网络嵌入,可以提高人类健康终点的预测准确度,帮助更安全的化学设计.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 机器学习 机器学习
背景情况:
- 化学空间网络 (CSN) 提供了一种新的方法来发现潜在的化学模式.
- 化学品安全网络可以加强对化学品潜在不良健康影响的评估.
- 现有的方法可能在全面描述化学毒性方面存在局限性.
研究的目的:
- 通过图形神经网络将化学空间网络结构嵌入到一大米空间中.
- 改善对各种人类健康终点的有毒和无毒化学品的歧视.
- 使用可解释的AI框架,为毒性预测提供可解释的结果.
主要方法:
- 使用分子描述符和指纹来构建CSN.
- 应用图形神经网络将 CSN 结构嵌入到一块尺度空间中.
- 采用可解释的人工智能 (XAI) 框架来解释结果.
主要成果:
- 改善了八个不同的毒理人类健康终点的分类性能.
- 预测性能的ROC曲线 (AUC) 下面的面积平均增加了12%.
- 确定与特定毒性相关的假定结构警报.
结论:
- 拟议的方法通过利用CSN嵌入来提高化学毒性的预测.
- 这种方法代表了化学安全评估的替代方法的重大进步.
- 这些发现可以推动在设计更安全的化学品和药品方面的创新.
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