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We focus on AI-based enzyme kinetic parameter prediction, with an emphasis on $\\mathit{k}_{\\text{cat}}$ and $\\mathit{K}_{\\text{m}}$, while also considering related parameters, such as $\\mathit{K}_{\\text{i}}$ and $\\mathit{k}_{\\text{cat}}\/\\mathit{K}_{\\text{m}}$. 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 $\\mathit{k}_{\\text{cat}}$ and $\\mathit{K}_{\\text{m}}$ 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.<\/jats:p>","DOI":"10.1093\/bib\/bbag533","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:25:38Z","timestamp":1789755938000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrative machine learning approaches for enzyme kinetic parameter prediction"],"prefix":"10.1093","volume":"27","author":[{"given":"Rui","family":"Zhou","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055 ,","place":["China"]},{"name":"University of Chinese Academy of Sciences , No. 19A Yuquan Road, Shijingshan District, Beijing 100049 ,","place":["China"]},{"name":"BGI Research , No. 59, Keji 3rd Road, Jiangxia District, Wuhan 430074 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Xi","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055 ,","place":["China"]},{"name":"University of Chinese Academy of Sciences , No. 19A Yuquan Road, Shijingshan District, Beijing 100049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Yin","sequence":"additional","affiliation":[{"name":"BGI Research , No. 59, Keji 3rd Road, Jiangxia District, Wuhan 430074 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Zelixir Biotech Co., Ltd. , 298 Xiangke Road, Pudong New Area, Shanghai 201210 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"BGI Research , No. 59, Keji 3rd Road, Jiangxia District, Wuhan 430074 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Wei","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055 ,","place":["China"]},{"name":"Faculty of Synthetic Biology, Shenzhen University of Advanced Technology , 1 Beizhen Road, Xinhu Subdistrict, Guangming District, Shenzhen 518055 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"2026100808250453700_ref1","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1126\/science.aay3060","article-title":"Designing for a green 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