Empowering Machines to Think Like Chemists: Unveiling Molecular
  Structure-Polarity Relationships with Hierarchical Symbolic Regression
Siyu Lou Chengchun Liu Yuntian Chen Fanyang Mo
Thin-layer chromatography (TLC) is a crucial technique in molecular polarity
analysis. Despite its importance, the interpretability of predictive models for
TLC, especially those driven by artificial intelligence, remains a challenge.
Current approaches, utilizing either high-dimensional molecular fingerprints or
domain-knowledge-driven feature engineering, often face a dilemma between
expressiveness and interpretability. To bridge this gap, we introduce
Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical
neural networks and symbolic regression. UHiSR automatically distills
chemical-intuitive polarity indices, and discovers interpretable equations that
link molecular structure to chromatographic behavior.