空洞的CoFe纳米酶与解性集成,通过机器学习设计用于瘤治疗
Feiyu Li1,2, Bocheng Xu2,3, Zijie Lu1,2
1State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China.
Small (Weinheim an der Bergstrasse, Germany)
|January 18, 2024
概括
机器学习模型确定了37种具有高致癌性和低正常细胞毒性新. 一种由C2和纳米酶H-CF组成的复合物有效地触发了瘤细胞中的ferroptosis,抑制了生长.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 计算生物学 计算生物学
背景情况:
- 开发用于纳米酶治疗的协同作用剂对于克服瘤异质性至关重要.
- 机器学习 (ML) 提供了一种强大的方法,可以加速发现有效的治疗剂.
研究的目的:
- 应用ML模型来识别低细胞毒性性.
- 开发一种新的-纳米酶复合物,用于增强癌症治疗.
主要方法:
- 使用穿越设计和Autogluon框架的过管道被用来选25740个序列.
- 用数据集测试和细胞毒性实验来训练和验证ML模型.
- C2与铁空心化物纳米酶 (H-CF) 结合,以创建一个复合材料.
主要成果:
- 确定了37种具有高瘤解毒活性和低正常细胞细胞毒性的新.
- ML管道在预测的有效性和安全性方面实现了80%的准确率.
- /H-CF复合物表现出酸触发的膜解功能和类似过氧化酶的活性,诱导瘤细胞铁亡并抑制瘤生长.
结论:
- ML辅助设计提供了一种准确而有效的方法来发现性.
- 开发的/H-CF复合物显示出作为向癌症治疗剂的巨大潜力.
- 这种方法为纳米医学中的催化材料设计协同提供了一个范式.
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