Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland.
Transgero Limited, Newcastle West, V42V384 Limerick, Ireland.
Int J Mol Sci. 2023 Feb 1;24(3):2792. doi: 10.3390/ijms24032792.
Reactive oxygen species (ROS) are compounds that readily transform into free radicals. Excessive exposure to ROS depletes antioxidant enzymes that protect cells, leading to oxidative stress and cellular damage. Nanomaterials (NMs) exhibit free radical scavenging efficiency representing a potential solution for oxidative stress-induced disorders. This study aims to demonstrate the application of machine learning (ML) algorithms for predicting the antioxidant efficiency of NMs. We manually compiled a comprehensive dataset based on a literature review of 62 in vitro studies. We extracted NMs' physico-chemical (P-chem) properties, the NMs' synthesis technique and various experimental conditions as input features to predict the antioxidant efficiency measured by a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Following data pre-processing, various regression models were trained and validated. The random forest model showed the highest predictive performance reaching an R = 0.83. The attribute importance analysis revealed that the NM's type, core-size and dosage are the most important attributes influencing the prediction. Our findings corroborate with those of the prior research landscape regarding the importance of P-chem characteristics. This study expands the application of ML in the nano-domain beyond safety-related outcomes by capturing the functional performance. Accordingly, this study has two objectives: (1) to develop a model to forecast the antioxidant efficiency of NMs to complement conventional in vitro assays and (2) to underline the lack of a comprehensive database and the scarcity of relevant data and/or data management practices in the nanotechnology field, especially with regards to functionality assessments.
活性氧 (ROS) 是易于转化为自由基的化合物。ROS 过度暴露会耗尽保护细胞的抗氧化酶，导致氧化应激和细胞损伤。纳米材料 (NMs) 表现出自由基清除效率，这代表了一种针对氧化应激诱导疾病的潜在解决方案。本研究旨在展示机器学习 (ML) 算法在预测 NMs 抗氧化效率方面的应用。我们基于对 62 项体外研究的文献综述，手动编制了一个综合数据集。我们提取了 NMs 的物理化学 (P-chem) 特性、NMs 的合成技术和各种实验条件作为输入特征，以预测通过 2,2-二苯基-1-苦基肼 (DPPH) 测定法测量的抗氧化效率。在进行数据预处理之后，我们训练和验证了各种回归模型。随机森林模型表现出最高的预测性能，达到 R = 0.83。属性重要性分析表明，NM 的类型、核心大小和剂量是影响预测的最重要属性。我们的研究结果与先前研究领域关于 P-chem 特征重要性的研究结果相吻合。本研究通过捕捉功能性能，将 ML 在纳米领域的应用扩展到了安全性相关结果之外。因此，本研究有两个目标：(1) 开发一种模型来预测 NMs 的抗氧化效率，以补充传统的体外测定法；(2) 强调缺乏全面的数据库以及纳米技术领域中相关数据和/或数据管理实践的缺乏，尤其是在功能评估方面。
