Interpretable Kolmogorov-Arnold networks for enzyme commission number prediction

Accurate prediction of enzyme commission (EC) numbers remains a significant challenge in bioinformatics, limiting our understanding of enzyme functions and their roles in biological processes. This paper presents the integration and evaluation of Kolmogorov-Arnold networks (KANs), a new deep learning paradigm, in state-of-the-art models for EC prediction. KAN modules are incorporated into existing models to assess their impact on predictive performance. Additionally, we introduce a novel interpretation method designed for KANs to identify relevant input features, addressing a key limitation of these networks. Our evaluation demonstrates that KAN integration substantially improves predictive accuracy, with up to a 15.7% increase in micro-averaged F1 score and a 34.2% increase in macro-averaged F1 score. Moreover, our interpretation method enhances the trustworthiness of predictions and facilitates the discovery of motif sites within enzyme sequences. This approach provides insight into enzyme functionality and highlights potential new targets for research. The code is available at: https://github.com/datax-lab/kan_ecnumber .