{"id":527053,"date":"2026-04-24T17:07:00","date_gmt":"2026-04-24T09:07:00","guid":{"rendered":"https:\/\/www.um.edu.mo\/news-and-press-releases\/press-release\/detail\/63849-2\/"},"modified":"2026-04-24T17:07:00","modified_gmt":"2026-04-24T09:07:00","slug":"63849","status":"publish","type":"post","link":"https:\/\/www.um.edu.mo\/pt-pt\/news-and-press-releases\/press-release\/detail\/63849\/","title":{"rendered":"UM develops AI platform to support TCM drug discovery for Alzheimer\u2019s disease"},"content":{"rendered":"\n                        \n                        <p>A research team led by Lu Jiahong,\ndeputy director of the Institute of\nChinese Medical Sciences (ICMS)\nat the\nUniversity of Macau (UM), in collaboration with Hangzhou MindRank AI Technology Co Ltd and Imperial\nCollege London in the UK, has developed an AI\nplatform that integrates a million-compound\nlibrary and multiple predictive modules for the discovery of bioactive\ningredients in traditional Chinese medicine (TCM)\nand natural products. The team\nused the platform\nfor virtual screening,\nfollowed by cross-species\nexperimental validation, to identify small-molecule\nTCM compounds with therapeutic potential for Alzheimer\u2019s disease (AD). The\nfindings have been published in the top-tier\ninternational journal <i>Nature\nBiomedical Engineering<\/i>.<\/p>\n\n<p>TCM and natural products have long been an important source of drug\ninnovation. However, the chemical complexity and vast number of TCM\nconstituents make traditional screening methods time-consuming\nand labour-intensive,\nhindering the modernisation of TCM. To overcome this bottleneck, the research team has developed an AI-driven discovery platform for TCM\nactive ingredients. The platform seamlessly integrates the team\u2019s previous research with cutting-edge\nAI technologies. Leveraging advanced algorithms and computational models, the platform can efficiently identify TCM ingredients with neuroprotective activity,\nautophagy-modulating properties, as well as favourable blood-brain\nbarrier (BBB) permeability. The platform has undergone two key iterations (the\nresults of the first generation were published in <i>Nature\nBiomedical Engineering<\/i> in 2022). The platform\u2019s natural product and TCM compound library\nhas grown to encompass millions of entries, and the speed and accuracy of screening have both been\nsignificantly improved. To date,\nseveral\ncandidate compounds have already\ndemonstrated promising\neffects in animal models of the disease and are currently undergoing further\ndevelopment.<\/p>\n\n<p>The AI platform is now publicly available (<a href=\"https:\/\/deepdrugdiscovery.mindrank.ai\/\">https:\/\/deepdrugdiscovery.mindrank.ai\/<\/a>). Its\naim is to facilitate the elucidation of\nTCM mechanisms, accelerate the\ndiscovery of innovative\ndrugs, and provide technical support and\na platform for the modernisation\nand internationalisation\nof TCM.<\/p>\n\n<p>Over 50 million\npeople worldwide suffer from AD.\nHowever, the field has long lacked major therapeutic breakthroughs. Drug\ndevelopment targeting traditional AD targets has faced persistent difficulties,\nand the amyloid-targeting\nantibody drugs approved in recent years offer only limited cognitive benefits and are associated with notable side effects.\nAutophagy, a highly conserved cellular \u2018waste clearance\u2019 mechanism that degrades and\nrecycles damaged proteins and old organelles, is essential for maintaining\nneuronal homeostasis. Numerous studies have shown that autophagic dysfunction in the brains of AD\npatients leads to the accumulation of toxic substances, such as <i>\u03b2<\/i>-amyloid and hyperphosphorylated\ntau, which exacerbate neurodegeneration. Therefore, restoring or enhancing\nautophagy is regarded as a highly promising new strategy for treating AD. The candidate\nTCM small molecules identified in\nthis study can precisely modulate an mTOR-independent\nautophagic pathway, promoting the clearance of toxic proteins and thereby\nslowing disease progression. This underscores the clinical relevance of\ntargeting autophagy as a therapeutic approach for AD.<\/p>\n\n<p>The corresponding authors of this study\nare Prof Lu and Niu Zhangming,\nCEO of Hangzhou MindRank AI Technology\nCo Ltd.\nDong Yu and Zhuang Xuxu, postdoctoral fellows at UM, as well\nas Xiao Xianglu and Yu Wenfan of Hangzhou\nMindRank AI Technology Co Ltd, are the co-first authors. Other contributors to the research include:\nShen Hanming, associate dean of FHS; Wan\nJianbo, deputy director of ICMS; Su Huanxing, professor in ICMS; and Yu Hua and Ouyang Defang, associate professors in ICMS. The\nresearch was funded by the University\nof Macau-Dr. Stanley Ho Medical Development\nFoundation \u201cSet Sail for New Horizons, Create\nthe Future\u201d Grant (Grant No.: SHMDF-OIRFS\/2024\/002), the General\nProgram of the National Natural Science Foundation of China (Grant No.: 82271455), the Science and\nTechnology Development Fund of the Macao SAR (Grant Nos.: 0040\/2024\/RIB1, 0002\/2025\/NRP),\nand the University of Macau Development Foundation (Grant Nos.: MYRG-GRG2024-00238-ICMS-UMDF, MYRG-GRG2023-00089-ICMS-UMDF). The full article can be\naccessed at: <a href=\"https:\/\/www.nature.com\/articles\/s41551-026-01667-x\">https:\/\/www.nature.com\/articles\/s41551-026-01667-x<\/a>.<\/p>\n\n<table style=\"width: 44.7597%;height: 201px\"> <tbody> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 100.095%\" colspan=\"2\">Source: Institute of Chinese Medical Sciences<\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 56.3278%;min-width: 120px\"> <\/td> <td style=\"height: 21px;width: 43.7669%\"> <\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 100.095%\" colspan=\"2\">Media Contact Information:<\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 12px;width: 100.095%\" colspan=\"2\">Communications Office, University of Macau<\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 56.3278%;min-width: 120px\"> <\/td> <td style=\"height: 21px;width: 43.7669%\"> <\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 56.3278%;min-width: 120px\">Albee Lei<\/td> <td style=\"height: 21px;width: 43.7669%\">Tel: (853) 8822 8004<\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 56.3278%;min-width: 120px\">Bell Leong<\/td> <td style=\"height: 21px;width: 43.7669%\">Tel: (853) 8822 8009<\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 56.3278%;min-width: 120px\">Email:<\/td> <td style=\"height: 21px;width: 43.7669%\"><a href=\"mailto:prs.media@um.edu.mo\">prs.media@um.edu.mo<\/a><\/td>\n<\/tr>\n<tr style=\"height: 21px\">\n<td style=\"height: 21px;width: 56.3278%;min-width: 120px\"> <\/td> <td style=\"height: 21px;width: 43.7669%\"> <\/td> <\/tr> <tr style=\"height: 21px\"> <td style=\"height: 21px;width: 56.3278%;min-width: 120px\"><\/td><td style=\"height: 21px;width: 43.7669%\"><\/td><\/tr>\n<\/tbody>\n<\/table>\n                        \n                        ","protected":false},"excerpt":{"rendered":"<p>A research team led by Lu Jiahong, deputy director of the Institute of Chinese Medical Sciences (ICMS) at the University of Macau (UM), in collaboration with Hangzhou MindRank AI Technology&#8230;<\/p>\n","protected":false},"author":1,"featured_media":527056,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[58],"tags":[],"class_list":["post-527053","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press-release"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>UM develops AI platform to support TCM drug discovery for Alzheimer\u2019s disease | Universidade de Macau<\/title>\n<meta name=\"description\" content=\"Universidade de Macau: Uma universidade abrangente p\u00fablica de n\u00edvel internacional fundada em 1981\" \/>\n<meta name=\"robots\" content=\"index, follow, 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