阿尔茨海默病(AD)构成了一个深刻的人类,社会,和经济负担。先前的研究表明,特级初榨橄榄油(EVOO)可能有助于预防认知能力下降。这里,我们提出了一种网络机器学习方法,用于识别EVOO中的生物活性植物化学物质,该方法具有影响与AD发展和进展相关的蛋白质网络的最高潜力。在五倍交叉验证设置中实现了70.3±2.6%的平衡分类准确性,用于预测来自其他临床批准药物的针对AD的晚期实验药物。然后使用校准的机器学习算法来预测现有药物和已知的EVOO植物化学物质在作用上与影响AD蛋白网络的药物相似的可能性。这些分析确定了以下十种具有抗AD活性的EVOO植物化学物质:槲皮素,Genistein,木犀草素,棕榈油酸盐,硬脂酸,芹菜素,表儿茶素,山奈酚,角鲨烯,和daidzein(从最高到最低可能性的顺序)。这项计算机模拟研究提出了一个将人工智能融合在一起的框架,分析化学,和组学研究,以确定独特的治疗剂。它提供了有关EVOO成分如何帮助治疗或预防AD的新见解,并可能为未来的临床研究提供基础。
Alzheimer\'s disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.