In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80–85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

中文翻译：

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网络机器学习绘制富含植物化学物质的“超级食物”以对抗 COVID-19

在本文中，我们介绍了一种网络机器学习方法，根据它们靶向 SARS-CoV-2-宿主基因-基因（蛋白质-蛋白质）相互作用组的能力来识别食物中潜在的生物活性抗 COVID-19 分子。我们的分析是使用超级计算 DreamLab App 平台进行的，利用了数千部智能手机的空闲计算能力。机器学习模型最初是通过证明所提出的方法可以预测针对 COVID-19 相互作用组学的实验和临床批准的药物（共 5658 种）中的抗 COVID-19 候选药物进行校准，平衡分类准确度为 5 倍，达到 80-85%交叉验证的设置。这确定了最有希望的候选药物，这些候选药物可以潜在地针对 COVID-19“重新利用”，包括用于对抗心血管和代谢疾病的常用药物，如辛伐他汀、阿托伐他汀和二甲双胍。一个包含 7694 个基于食物的生物活性分子的数据库通过校准的机器学习算法运行，该算法从不同的化学类别中识别出 52 个生物活性分子，包括黄酮类化合物、萜类化合物、香豆素和吲哚，预计以 SARS-CoV-2 宿主相互作用网络为目标. 这反过来又被用来构建“食物地图”，根据具有抗病毒特性的候选化合物的多样性和相对水平估计每种成分的理论抗 COVID-19 潜力。