对化学线粒体毒性的结构特征的建模和洞察力
Ruiqiu Zhang1, Zhaoyang Chen1, Baobao Wang2
1Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
ACS omega
|September 11, 2023
概括
人工智能模型现在可以预测化学线粒体毒性. 这项研究开发了准确的AI工具来识别有毒化合物并了解它们的结构原因,帮助药物安全性评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
- 生物化学 生化学
背景情况:
- 线粒体对于细胞能量和亡和增殖等过程至关重要.
- 评估化学和药物线粒体毒性对于安全至关重要.
- 现有的毒性评估方法可能耗时且资源密集.
研究的目的:
- 开发可访问的人工智能 (AI) 模型,用于预测化学线粒体毒性.
- 确定与线粒体毒性相关的关键结构特征和分子特性.
- 提供用于in silico毒性估计的计算工具.
主要方法:
- 使用分子性质和结构警报的共识AI模型的开发.
- 分析化学特性,区分有毒与无毒化合物.
- 将已识别的结构性警报集成到网络服务器 (SApredictor).
主要成果:
- 一个共识AI模型在预测线粒体毒性方面实现了87.21%的总准确性.
- 在有毒和无毒化学品之间观察到常见化学性质的显著差异.
- 与线粒体毒性相关的结构性警报被确定并集成到预测工具中.
结论:
- 开发的人工智能模型提供了一种可靠的方法,用于在线粒体中毒性的状估计.
- 该研究为早期药物和化学品安全评估提供了有价值的工具.
- 了解结构性警报有助于阐明线粒体毒性的机制.
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