KAN-based symbolic QSPR modeling for molecular property prediction

Molecular property prediction faces a persistent trade-off between predictive accuracy and model interpretability. Graph neural networks achieve high accuracy through end-to-end learning from molecular graphs, but their predictions are difficult to interpret. Traditional QSPR methods produce interpretable linear equations, yet cannot capture nonlinear structure-property relationships. This paper proposes a QSPR framework based on the Kolmogorov-Arnold Network (KAN) that uses a small set of explicit molecular descriptors as input, models nonlinear relationships through learnable B-spline activation functions, and extracts symbolic equations from the trained network. Experiments on eight benchmark datasets show that KAN achieves competitive performance with graph neural networks while using only approximately six explicit descriptors on average. The extracted symbolic equations substantially outperform traditional linear QSPR formulas in fitting ability, and the learned functional forms (e.g., logarithmic, exponential) are consistent with established chemical knowledge. These results demonstrate that KAN offers a viable middle ground between purely linear QSPR models and black-box deep learning.