{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:41:30Z","timestamp":1760031690599,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Russian Federation","award":["FSSW-2025-0004"],"award-info":[{"award-number":["FSSW-2025-0004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The interaction of estrogen receptor alpha (ER\u03b1) with various metabolites\u2014both endogenous and exogenous, such as those present in food products, as well as gut microbiota-derived metabolites\u2014plays a critical role in modulating the hormonal balance in the human body. In this study, we evaluated a suite of 27 machine learning models and, following systematic optimization and rigorous performance comparison, identified linear discriminant analysis (LDA) as the most effective predictive approach. A meticulously curated dataset comprising 75 molecular descriptors derived from compounds with known ER\u03b1 activity was assembled, enabling the model to achieve an accuracy of 89.4% and an F1 score of 0.93, thereby demonstrating high predictive efficacy. Feature importance analysis revealed that both topological and physicochemical descriptors\u2014most notably FractionCSP3 and AromaticProportion\u2014play pivotal roles in the potential binding to ER\u03b1. Subsequently, the model was applied to chemicals commonly encountered in food products, such as indole and various phenolic compounds, indicating that approximately 70% of these substances exhibit activity toward ER\u03b1. Moreover, our findings suggest that food processing conditions, including fermentation, thermal treatment, and storage parameters, can significantly influence the formation of these active metabolites. These results underscore the promising potential of integrating predictive modeling into food technology and highlight the need for further experimental validation and model refinement to support innovative strategies for developing healthier and more sustainable food products.<\/jats:p>","DOI":"10.3390\/bdcc9040086","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T16:19:16Z","timestamp":1743610756000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Predictive Model for the Biological Activity of Food and Microbial Metabolites Toward Estrogen Receptor Alpha (ER\u03b1) Using Machine Learning"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2652-0303","authenticated-orcid":false,"given":"Maksim","family":"Kuznetsov","sequence":"first","affiliation":[{"name":"Department of Food Technology and Bioengineering, Plekhanov Russian University of Economics, 36 Stremyanny per., 115054 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7163-6903","authenticated-orcid":false,"given":"Olga","family":"Chernyavskaya","sequence":"additional","affiliation":[{"name":"Department of Food Technology and Bioengineering, Plekhanov Russian University of Economics, 36 Stremyanny per., 115054 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1330-9782","authenticated-orcid":false,"given":"Mikhail","family":"Kutuzov","sequence":"additional","affiliation":[{"name":"Research Laboratory of Applied Biotechnology, Cherepovets State University, 5 Lunacharsky pr., 162602 Cherepovets, Russia"},{"name":"Apq Control LLC, Office 11, 45 Pervomajskaya st., 162612 Cherepovets, Russia"}]},{"given":"Daria","family":"Vilkova","sequence":"additional","affiliation":[{"name":"Research Laboratory of Applied Biotechnology, Cherepovets State University, 5 Lunacharsky pr., 162602 Cherepovets, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4282-9728","authenticated-orcid":false,"given":"Olga","family":"Novichenko","sequence":"additional","affiliation":[{"name":"Research Laboratory of Applied Biotechnology, Cherepovets State University, 5 Lunacharsky pr., 162602 Cherepovets, Russia"},{"name":"Department of Biotechnology, Aquaculture, Soil Science and Land Management, Astrakhan Tatishchev State University, 20a, Tatishchev st., 414056 Astrakhan, Russia"}]},{"given":"Alla","family":"Stolyarova","sequence":"additional","affiliation":[{"name":"Basic Department of Trade Policy, Plekhanov Russian University of Economics, 36 Stremyanny per., 115054 Moscow, Russia"},{"name":"Department of Management and Economics, State University of Humanities and Social Studies, 30 Zelenaya st., 140400 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1803-3033","authenticated-orcid":false,"given":"Dmitry","family":"Mashin","sequence":"additional","affiliation":[{"name":"Department of Management and Economics, State University of Humanities and Social Studies, 30 Zelenaya st., 140400 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8988-5911","authenticated-orcid":false,"given":"Igor","family":"Nikitin","sequence":"additional","affiliation":[{"name":"Department of Food Technology and Bioengineering, Plekhanov Russian University of Economics, 36 Stremyanny per., 115054 Moscow, Russia"},{"name":"Research Laboratory of Applied Biotechnology, Cherepovets State University, 5 Lunacharsky pr., 162602 Cherepovets, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, H., and Gollahon, L.S. 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