Integrative machine learning approaches for enzyme kinetic parameter prediction

The prediction of enzyme kinetic parameters, particularly kcat and Kₘ, is rapidly evolving from physics-based modeling toward data-driven artificial intelligence (AI) approaches, driven by expanding biochemical databases and advances in machine learning (ML) and deep learning (DL). We focus on AI-based enzyme kinetic parameter prediction, with an emphasis on kcat and Kₘ, while also considering related parameters, such as Kᵢ and kcat/Kₘ. The key findings reveal that: (i) Representation learning increasingly relies on pretrained protein language models, while structural embeddings often show limited additional performance gains over sequence-based approaches; (ii) Model performance is highly data-dependent: classical ML regressors can outperform DL architectures on small datasets when using comparable feature representations, though multimodal feature integration can improve generalization; and (iii) Multitask frameworks for the joint prediction of kcat and Kₘ may provide synergistic benefits, although the magnitude of the improvement may be limited. Future progress will require standardized benchmarks, mechanistically informed model architectures, uncertainty-aware prediction, and robust out-of-distribution validation to improve biochemical relevance, interpretability, and real-world utility.