走向可解释的计算毒理学:将反目标与动物急性毒性联系起来
Ilia Nikitin1, Igor Morgunov1,2, Victor Safronov1,3
1Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoy Karetny per. 19, Moscow 127051, Russia.
Pharmaceutics
|December 31, 2025
概括
这项研究引入了一个新的数据集,将化学化合物与小鼠的LD50值和蛋白质相互作用联系起来,有助于开发可解释的毒性预测模型.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 药理学 药理学 药理学 药理学 药理学
背景情况:
- 开发可解释的预测工具是计算毒理学的关键趋势.
- 挑战包括机械复杂性和数据稀缺性.
研究的目的:
- 创建一个公开可用的数据集,以推进透明,机制意识的毒性建模.
- 调查化合物-蛋白相互作用和急性毒性之间的关联.
主要方法:
- 编制了12654种化合物的数据集,与小鼠静脉输入的LD50值.
- 对44种与毒性相关的蛋白进行了对接模拟 (Vina-GPU 2.0) .
- 应用NIH和Brenk过器来完善化学空间.
主要成果:
- 确定了急性毒性与特定蛋白质之间的强烈关联:hERG/KCNH2,AVPR1A,CACNA1C,KCNQ1和EDNRA.
- 观察到与抗目标结合的化合物和与非结合剂结合的化合物之间的LD50值存在显著差异.
- 证明了反向对接的实用性,用于使用已知的生物活性分子阐明作用机制.
结论:
- 开发的数据集是透明和机制意识的毒性建模的宝贵资源.
- 开放的数据有助于进一步研究预测毒理学.
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