Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently “biorelevant,” because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol’s antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F₂-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.

由过量的活性氧 (ROS) 引起的氧化应激是多种疾病的基础。因此，对抗氧化损伤的治疗策略是预防和治疗此类疾病的深入科学研究的主题，其中植物化学抗氧化剂，尤其是多酚的使用是主要部分。然而，多酚表现出的结构多样性决定了抗氧化作用的不同机制，例如氢原子转移 (HAT) 和单电子转移 (SET)。它们的体内生物利用度也不足，其抗氧化生物活性受渗透性、肠壁和首过代谢以及基于 HAT 的 ROS 捕获控制。不幸的是，目前还没有抗氧化剂分析能够捕捉到这些多个维度，使其具有足够的“生物相关性，”因为分析往往是一维的，而生物相关性需要整合多个输入。因此，寻找一种可靠地评估这些植物化学物质的抗氧化能力的方法仍然是一个未满足的需求。为了解决这一缺陷，我们建议使用基于人工智能 (AI) 的机器学习 (ML) 将作为输出变量的多酚的抗氧化作用与作为输入变量的分子描述符（控制体内抗氧化活性的因素）联系起来，在脂质过氧化选择性产生的生物标志物（氧化应激的结果），例如 F_2-异前列腺素。支持向量机、人工神经网络和贝叶斯概率学习是一些可以部署的关键算法。这样的模型将代表一个强大的预测工具，用于评估多酚的生物相关抗氧化能力，从而促进抗氧化分子的识别或设计。该方法还将有助于实现在生物医学研究中使用动物的 3R（替代、减少和改进）原则。