Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil

Aug 4, 2026

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Alzheimer’s disease (AD) carries substantial human, social, and economic burden, and its mechanism is still not fully understood, with no therapy that reverses or prevents neurocognitive decline. Extra virgin olive oil (EVOO) has been linked to neuroprotective benefits, but the responsible phytochemicals remain unclear. This study proposes a network machine learning framework to identify EVOO phytochemicals with high potential to affect AD-linked protein networks. A calibrated model predicts AD-targeting drugs and ranks ten EVOO phytochemicals by likelihood of similar action, supporting AI-analytical chemistry-omics integration for future therapeutic exploration.

LanguageEnglish

CategoryResearch & Report

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Pages11

Reading Time28 min

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Abstract

Introduction

Rita et al. Human Genomics (2023) 17:57 [https://doi.org/10.1186/s40246-023-00503-6](https://doi.org/10.1186/s40246-023-00503-6) Human Genomics RESEARCH Open Access Alzheimer’s disease: using gene/protein network machine learning for molecule discovery in olive oil Luís Rita 1†, Natalie R. Neumann2†, Ivan Laponogov 1, Guadalupe Gonzalez3,4, Dennis Veselkov1, Domenico Pratico5, Reza Aalizadeh7, Nikolaos S. Thomaidis7, David C. Thompson6, Vasilis Vasiliou6* and Kirill Veselkov1,6* Abstract 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 ofthe 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. Keywords Olive oil, Alzheimer’s disease, Network propagation, Nutrition †Luís Rita and Natalie Neumann contributed equally to this work. *Correspondence: Vasilis Vasiliou [vasilis.vasiliou@yale.edu](vasilis.vasiliou@yale.edu)[ ](vasilis.vasiliou@yale.edu)Kirill Veselkov [kirill.veselkov04@imperial.ac.uk](kirill.veselkov04@imperial.ac.uk) 1 Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK 2 Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA 3 Department of Computing, Faculty of Engineering, Imperial College London, London, UK 4 Prescient Design, Genentech | Roche, Basel, Switzerland 5 Alzheimer’s Center at Temple, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA 6 Department of Environmental Health Sciences, Yale University, New Haven, CT, USA 7 Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver

FAQs

本文研究要解决的核心问题是什么？

尽管EVOO可能有助于预防认知衰退，但其具体发挥作用的植物化学成分尚不清楚，因此需要识别更可能影响与AD相关蛋白网络的活性分子。

研究如何利用基因/蛋白网络机器学习来发现潜在分子？

通过网络机器学习识别EVOO中具有最高潜力影响AD进展相关蛋白网络的生物活性植物化学成分，并在模型校准后预测已知药物与EVOO成分在作用方式上的相似性。

模型预测的性能指标与结果有哪些？

在五折交叉验证设置中，预测晚期实验药物（靶向AD蛋白网络）相对于其他临床批准药物的分类准确率达到70.3 ± 2.6%。随后预测出10种EVOO植物化学成分中对AD活性可能性最高的候选物，并按可能性从高到低排序。

Aug 4, 2026

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