Integrating Arrhenius Constraints with Lineage-Aware Meta-Learning for Few-Shot Prediction of Temperature-Dependent Enzyme Kinetics

). On a rigorous low-redundancy benchmark, PIMetaKcat achieves high fidelity (0.957 ± 0.021, 0.565 ± 0.032), demonstrating superior generalization to low-similarity sequences compared to existing baselines. Furthermore, PIMetaKcat autonomously distinguishes distinct thermal niches and enables high-precision ranking of mutant libraries. As a physics-anchored engine, it accelerates the Design-Build-Test (DBT) cycle for rational enzyme design. The code and data are openly available at https://github.com/QiTiaotiao2/PIMetaKcat.