分子表示的拓及其对机器学习性能的影响
Florian Rottach1,2, Sebastian Schieferdecker3, Carsten Eickhoff4
1Central Data Science, Boehringer Ingelheim GmbH, Biberach/Riss, Germany. florian.rottach@boehringer-ingelheim.com.
Journal of cheminformatics
|July 22, 2025
概括
本研究介绍了TopoLearn,这是一个模型,使用特征空间拓学预测分子表示效率. 它将持久的同类描述符与机器学习错误联系起来,有助于表示选择.
科学领域:
- 化学信息学 化学信息学
- 机器学习 机器学习
- 计算化学计算化学
背景情况:
- 存在许多数值分子编码方法,但选择化学数据集的最佳表示是具有挑战性的.
- 当前的选择过程往往是经验和计算密集的,缺乏系统的方法.
- 深度学习模型不总是优于分子数据的传统方法,原因不清楚.
研究的目的:
- 开发一种系统的方法来选择最佳的分子表示.
- 根据数据集拓预测分子表示的有效性.
- 了解特征空间拓与机器学习模型性能之间的关系.
主要方法:
- 开发了TopoLearn,这是一个分析特征空间拓特征的模型.
- 利用持久的同质描述符来表征特征空间.
- 应用可解释性技术,将拓特征与机器学习模型错误指标联系起来.
主要成果:
- TopoLearn成功地预测了分子表示的有效性.
- 持久的同类描述符与机器学习模型错误指标相关.
- 建立了特征空间拓与表示性能之间的经验联系.
结论:
- TopoLearn提供了一种新的,以拓学为驱动的方法来选择分子表示.
- 了解特征空间拓学可以提高化学信息学工具的解释性和选择性.
- 这项工作提供了开放式访问工具,以推进分子表示和机器学习方面的研究.
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