针对群体向的化顺序预测的机器学习:替代的黄作为一个案例研究
Ivan Rozanov1,2, Andrey Stavrianidi1,2, Aleksey Buryak1
1A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, Moscow 119071, Russia.
Journal of chemical information and modeling
|November 17, 2025
概括
机器学习模型准确地预测了植物代谢物分析的化顺序. 这种方法有助于通过了解它们的结构如何影响色谱保留时间来识别化合物.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 准确预测植物代谢物的化顺序对于脱复制至关重要.
- 机器学习 (ML) 提供了一种高效的方法,可以使用结构-保留关系来自动化峰值注释.
研究的目的:
- 训练和验证ML模型,用于预测黄类衍生物对的化顺序.
- 探索分子指纹和训练策略对预测准确性的影响.
主要方法:
- 四个ML模型 (神经网络排名,物流回归) 使用一种新的分子指纹进行训练.
- 从文献数据中构建了两个数据集 (51个化合物,48个化合物).
- 使用线性回归和插值间接估计了保留时间.
主要成果:
- 在逆相LC条件下,预测化顺序的对错误率始终低于10%.
- 线性回归模型显示了轻微的性能优势,得到了统计验证.
- 较大,统一的数据集被证明比分散的数据集更有利于模型培训.
结论:
- ML模型,特别是线性回归模型,在预测弗拉衍生物的化顺序方面显示出可靠的性能.
- 开发的分子指纹有效地编码了植物化学分析的结构特征.
- 通过模型重量可视化来增强对染色体保留的替代物效应的理解.
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