[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126435-en":3,"doc-seo-126435-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126435,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework","KmPred presents an integrative machine learning framework for predicting Michaelis–Menten constants (Km), addressing the high cost of traditional in vitro kinetic assays. The approach combines protein sequence embeddings from state-of-the-art language models with molecular descriptors generated from substrate SMILES. Performance is evaluated on the MPEK dataset and an independent Kroll dataset. Results show strong accuracy on both datasets, demonstrating robust and generalizable Km prediction via multimodal features and an XGBoost regression model supported by LSTM/Transformer feature extraction.","TYPE Original Research PUBLISHED 30 January 2026 DOI 10.3389/frai.2026.1711471  \nOPEN ACCESS  \nEDITED BY  \nDharmendra Kumar Yadav,  \nGachon University, Republic of Korea  \nREVIEWED BY  \nRonan M. T. Fleming, University of Galway, Ireland Miguel Rocha,  \nUniversity of Minho, Portugal  \n*CORRESPONDENCE  \nMeshari Alazmi  \n [ms.alazmi@uoh.edu.sa](ms.alazmi@uoh.edu.sa)  \nRECEIVED 23 September 2025  \nREVISED 12 January 2026  \nACCEPTED 16 January 2026  \nPUBLISHED 30 January 2026  \nCITATION  \nAlazmi M (2026) KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework.  \nFront. Artif. Intell. 9:1711471 .  \ndoi: 10.3389/frai.2026.1711471  \nCOPYRIGHT  \n© 2026 Alazmi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nKmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework  \nMeshari Alazmi*  \nCollege of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia  \nBackground and motivation: The Michaelis constant Km is one of the key kinetic parameters in the quantification of enzyme-substrate affinity within the context of the Michaelis–Menten theory. While Km values are traditionally subjected to laborintensive governance via in vitro assays, the brisk expansion of protein sequence and chemical databases has composed an essential intended for computational prediction approaches.  \nMethodology: Herein, we expose a consolidative machine learning frameworkKmPred-for Km forecast that merges protein sequence embeddings from stateof-the-art language models with molecular descriptors derived from substrate SMILES descriptions. This methodology was benchmarked on the MPEK dataset and the independent dataset assembled by Kroll et al.  \nResults and discussion: On the MPEK dataset, the greatest model achieved a test MSE of 0.4995, RMSE of 0.7067, MAE of 0. 5022, R2 of 0.7049, and a PCC of 0.8398 (p \u003C 1 × 10−6), outperforming the baseline MPEK model. On the Kroll dataset, KmPred achieved a test MSE of 0.6206, RMSE of 0.7878, R2 of 0. 5519, PCC of 0.7440, and Spearman’s ρ of 0.7342, which represents reasonable results compared to state-of-the-art methods. These outcomes demonstrate that combining multi-modal protein sequence and ligand features with advanced machine learning architectures enables robust and generalizable Km prediction across diverse datasets. Specifically, we utilized LSTM and Transformer models solely for feature extraction to capture complex sequential and contextual patterns from enzyme sequences, while employing XGBoost as our primary regression model for final Km predictions. Beyond methodological impact, this work highlights the role of AI-driven kinetic modeling in accelerating enzyme characterization, facilitating metabolic engineering, and enhancing drug discovery pipelines. Our approach thus establishes a foundation for predictive enzymology at scale, with significant potential to benefit biotechnology, synthetic biology, and national strategic initiatives such as Saudi Vision 2030.  \nKEYWORDS  \nbioinformatics, drug discovery, KmPred, metabolic engineering, Michaelis–Menten constant (Km), molecular descriptors, protein embeddings, systems biology  \nIntroduction  \nEnzymes are necessary biological catalysts, transforming, in effect, all biochemical pathways-from central carbon metabolism down to detailed biosynthetic pathways-through letting down of activation energy into physiologically applicable periods and giving exquisite control over cellular metabolism. One of the desperate parameters that outline enzyme activity is the Michaelis–Menten c","cbCaicVR1HOToiG7","https://ap.wps.com/l/cbCaicVR1HOToiG7","pdf",1698210,6,1,9,"English","en",105,"# Background and motivation\n# Methodology\n# Results and discussion\n## Evaluation on MPEK dataset\n## Evaluation on Kroll dataset\n# Introduction","[{\"question\":\"What problem does KmPred address in enzyme kinetics?\",\"answer\":\"KmPred targets efficient prediction of the Michaelis–Menten constant (Km), which normally requires labor-intensive in vitro assays to quantify enzyme–substrate affinity.\"},{\"question\":\"How does KmPred combine protein and ligand information?\",\"answer\":\"KmPred merges protein sequence embeddings from advanced language models with molecular descriptors derived from substrate SMILES, enabling multimodal learning for Km prediction.\"},{\"question\":\"What model architecture does KmPred use for final Km prediction?\",\"answer\":\"KmPred uses LSTM and Transformer models for feature extraction from enzyme sequences, and applies XGBoost as the primary regression model for final Km predictions.\"}]","KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework | 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problem does KmPred address in enzyme kinetics?","Question",{"text":77,"@type":78},"KmPred targets efficient prediction of the Michaelis–Menten constant (Km), which normally requires labor-intensive in vitro assays to quantify enzyme–substrate affinity.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does KmPred combine protein and ligand information?",{"text":82,"@type":78},"KmPred merges protein sequence embeddings from advanced language models with molecular descriptors derived from substrate SMILES, enabling multimodal learning for Km prediction.",{"name":84,"@type":75,"acceptedAnswer":85},"What model architecture does KmPred use for final Km prediction?",{"text":86,"@type":78},"KmPred uses LSTM and Transformer models for feature extraction from enzyme sequences, and applies XGBoost as the primary regression model for final Km 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