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Thin-layer chromatography (TLC) is a crucial technique in molecular polarityanalysis. Despite its importance, the interpretability of predictive models forTLC, especially those driven by artificial intelligence, remains a challenge.Current approaches, utilizing either high-dimensional molecular fingerprints ordomain-knowledge-driven feature engineering, often face a dilemma betweenexpressiveness and interpretability. To bridge this gap, we introduceUnsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchicalneural networks and symbolic regression. UHiSR automatically distillschemical-intuitive polarity indices, and discovers interpretable equations thatlink molecular structure to chromatographic behavior.

Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression