由物理化学原理指导的机器学习使小分子亚细胞定位的概括预测和向分子的发现成为可能
Cong Liu1, Jie Chen1, Shan He1
1College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China.
Analytical chemistry
|October 17, 2025
概括
这项研究引入了一种机器学习模型,用于预测小分子细胞器官的定位,这对于开发向治疗和分子探针至关重要. 该模型达到高精度,指导创建具有特定细胞功能的新型探针.
科学领域:
- 化学生物学 化学生物学
- 计算化学计算化学
- 分子成像学分子成像学
背景情况:
- 精确的亚细胞定位对于设计分子探针和向治疗方法至关重要.
- 区分具有相似物理化学性质的有机体 (例如,脂质滴,线粒体,细胞膜) 是一个挑战.
- 传统方法缺乏通用性,并与复杂的结构-活动关系作斗争.
研究的目的:
- 开发一种创新的机器学习 (ML) 策略,用于精确预测小分子有机体定位.
- 克服传统的经验方法在预测分子分离的局限性.
- 引导设计和合成具有特定有机细胞向的新型化学探针.
主要方法:
- 开发了一种ML模型,学习控制分子分离的基本物理化学力量.
- 使用了355个样本的适度数据集进行培训.
- 前性地使用该模型来选探针库和指导合成.
主要成果:
- 在小分子局部化方面取得了极高的预测准确度 (>94%的交叉验证).
- 成功设计和合成了三个聚合诱导的排放探测器,具有预测的有机细胞向特异性.
- 通过确认探测器定位和解释异常准行为来证明模型概括.
结论:
- 在物理化学原理的指导下,ML可以准确地预测器官位.
- 这种方法使化学生物学研究和药物发现成为可能,因为它使功能化学工具的高效开发成为可能.
- 开发的模型为创建向治疗和分子探针提供了一个可概括的范式.
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