在复杂的多变量系统中用于可解释性质建模的无监督层次符号回归
Siyu Lou1,2, Chengchun Liu3, Dongxiao Zhang2
1School of computer science, Shanghai Jiao Tong University, Shanghai, P.R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 7, 2026
概括
无监督层次符号回归 (UHSR) 为化学分析提供了可解释的AI方法,成功将分子结构与薄层染色学 (TLC) 中的染色学行为联系起来,并获得了化学家的信任.
科学领域:
- 人工智能的人工智能
- 化学信息学 化学信息学
- 分析化学 分析化学
背景情况:
- 人工智能模型擅长化学分析预测,但往往缺乏可解释性.
- 薄层染色学 (TLC) 对于分析分子极性至关重要.
- 需要可解释的人工智能来建立对预测化学模型的信任.
研究的目的:
- 引入无监督层次符号回归 (UHSR) 作为可解释的AI解决方案.
- 开发一个模型,保持竞争力的预测性能.
- 展示UHSR获得化学直观见解的能力.
主要方法:
- UHSR自动从TLC数据中提取保留指数.
- UHSR发现了可解释的方程,将分子结构与染色学行为联系起来.
- 评估了该模型对其他财产预测任务的适应性.
主要成果:
- 从TLC数据中,UHSR成功地得出了用于极性预测的简洁而准确的方程.
- 专家化学家表示,与传统模型相比,他们更信任UHSR.
- 该方法显示了超出分子极性预测的适应性.
结论:
- UHSR为化学预测建模提供了一个强大而可解释的替代方案.
- 在化学中,可解释的AI可以增强模型的信任和实用性.
- 在化学信息学和分析化学中,UHSR具有广泛的应用.
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