{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:08:39Z","timestamp":1790698119939,"version":"4.1.0"},"reference-count":32,"publisher":"American Chemical Society (ACS)","issue":"38","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82574701"],"award-info":[{"award-number":["82574701"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disease characterized by excessive accumulation of \u03b2-amyloid (A\u03b2) peptides in the brain. Beta-secretase 1 (BACE1), the rate-limiting enzyme in the amyloidogenic processing pathway of amyloid precursor protein (APP), has been widely recognized as an important therapeutic target for reducing A\u03b2 production. In the present study, nine machine learning-based predictive models for BACE1 inhibitors were constructed using Nai\u0308ve Bayes (NB) and Recursive Partitioning (RP) algorithms. Subsequently, internal and external validations were conducted to evaluate predictive performance. The optimized models were subsequently applied to screen an in-house natural product library containing 452 compounds. Based on consensus prediction criteria, 171 compounds were identified as potential BACE1 inhibitors and subjected to fluorescence resonance energy transfer (FRET)-based enzymatic assays. Among them, 33 compounds exhibited inhibitory rates greater than 50% at an initial concentration of 50 \u03bcg\/mL. Further dose\u2013response evaluation identified five compounds with IC50 values below 50 \u03bcM, including Theaflavin-3,3\u2032-digallate, \u03b3-mangostin, Licoagrochalcone B, Chicoric acid, and Resveratrol. Cell-based validation using APPswe-HEK293T cells demonstrated that Theaflavin-3,3\u2032-digallate significantly reduced extracellular A\u03b21\u201342 secretion. Molecular docking analysis further revealed that Theaflavin-3,3\u2032-digallate could stably bind to BACE1 through multiple hydrogen-bond and hydrophobic interactions. Collectively, this study demonstrates that integrating machine learning-assisted virtual screening with natural product libraries is an effective strategy for discovering novel BACE1 inhibitors. Theaflavin-3,3\u2032-digallate was identified as a promising natural BACE1 inhibitor with both enzymatic and cellular activities. This research provides new technology and potential lead compound for the development of future anti-Alzheimer\u2019s disease drug.<\/jats:p>","DOI":"10.1021\/acsomega.6c07963","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T10:01:52Z","timestamp":1789552912000},"page":"58100-58111","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Guided\nDiscovery of Natural BACE1\nInhibitors with Enzymatic and Cellular Validation"],"prefix":"10.1021","volume":"11","author":[{"given":"Yiming","family":"Bai","sequence":"first","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]},{"name":"The First Affiliated Hospital of Nankai University , , ,","place":["Tianjin, China, 300121"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shurong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6807-0850","authenticated-orcid":true,"given":"Caiqin","family":"Yan","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanhua","family":"Du","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1989-103X","authenticated-orcid":true,"given":"Ailin","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College , , ,","place":["Beijing, China, 100050"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"2026092911100776600_cit1","doi-asserted-by":"publisher","first-page":"102342","DOI":"10.1016\/j.arr.2024.102342","article-title":"Navigating the Maze of Alzheimer\u2019s disease by exploring BACE1:\nDiscovery, current scenario, and future prospects","volume":"98","author":"Iram","year":"2024","journal-title":"Ageing Res. 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S.\nA"},{"issue":"3","key":"2026092911100776600_cit28","doi-asserted-by":"publisher","first-page":"467","DOI":"10.3390\/molecules24030467","article-title":"Theaflavins Improve Memory Impairment and Depression-Like Behavior\nby Regulating Microglial Activation","volume":"24","author":"Ano","year":"2019","journal-title":"Molecules"},{"issue":"3","key":"2026092911100776600_cit29","doi-asserted-by":"publisher","first-page":"512","DOI":"10.3390\/molecules23030512","article-title":"Neuroprotective\nEffects and Mechanisms\nof Tea Bioactive Components in Neurodegenerative Diseases","volume":"23","author":"Chen","year":"2018","journal-title":"Molecules"},{"issue":"49","key":"2026092911100776600_cit30","doi-asserted-by":"publisher","first-page":"10624","DOI":"10.1021\/bi2012383","article-title":"Black tea theaflavins inhibit formation\nof toxic amyloid-\u03b2\nand \u03b1-synuclein fibrils","volume":"50","author":"Grelle","year":"2011","journal-title":"Biochemistry"},{"issue":"5","key":"2026092911100776600_cit31","doi-asserted-by":"publisher","first-page":"2356","DOI":"10.1021\/acs.jafc.2c06679","article-title":"Theaflavins\nin Black Tea Mitigate\nAging-Associated Cognitive Dysfunction via the Microbiota-Gut-Brain\nAxis","volume":"71","author":"Li","year":"2023","journal-title":"J. Agric. Food Chem"},{"key":"2026092911100776600_cit32","doi-asserted-by":"publisher","first-page":"42717","DOI":"10.1038\/srep42717","article-title":"SwissADME: a free web tool to evaluate\npharmacokinetics, drug-likeness and medicinal chemistry friendliness\nof small molecules","volume":"7","author":"Daina","year":"2017","journal-title":"Sci. Rep"}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.6c07963","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/38\/58100\/68528069\/acsomega.6c07963.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/38\/58100\/68528069\/acsomega.6c07963.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:50:49Z","timestamp":1790697049000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/11\/38\/58100\/5431051\/Machine-Learning-Guided-Discovery-of-Natural-BACE1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":32,"journal-issue":{"issue":"38","published-online":{"date-parts":[[2026,9,16]]},"published-print":{"date-parts":[[2026,9,29]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.6c07963","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]}}}