Electrical Engineering Department, Badr University in Cairo, Badr City, Cairo 11829, Egypt.
Badr University in Cairo Research Center, Badr University in Cairo, Badr City, Cairo 11829, Egypt.
Methods. 2023 Oct;218:133-140. doi: 10.1016/j.ymeth.2023.08.008. Epub 2023 Aug 16.
Exploitation of machine learning in predicting performance of nanomaterials is a rapidly growing dynamic area of research. For instance, incorporation of therapeutic cargoes into nanovesicles (i.e., entrapment efficiency) is one of the critical parameters that ensures proper entrapment of drugs in the developed nanosystems. Several factors affect the entrapment efficiency of drugs and thus multiple assessments are required to ensure drug retention, and to reduce cost and time. Supervised machine learning can allow for the construction of algorithms that can mine data available from earlier studies to predict performance of specific types of nanoparticles. Comparative studies that utilize multiple regression algorithms to predict entrapment efficiency in nanomaterials are scarce. Herein, we report on a detailed methodology for prediction of entrapment efficiency in nanomaterials (e.g., niosomes) using different regression algorithms (i.e., CatBoost, linear regression, support vector regression and artificial neural network) to select the model that demonstrates the best performance for estimation of entrapment efficiency. The study concluded that CatBoost algorithm demonstrated the best performance with maximum R score (0.98) and mean square error (< 10). Among the various parameters that possess a role in entrapment efficiency of drugs into niosomes, the results obtained from CatBoost model revealed that the drug:lipid ratio is the major contributing factor affecting entrapment efficiency, followed by the lipid:surfactant molar ratio. Hence, supervised machine learning may be applied for future selection of the components of niosomes that achieve high entrapment efficiency of drugs while minimizing experimental procedures and cost.
机器学习在预测纳米材料性能方面的应用是一个快速发展的研究领域。例如，将治疗性货物包封入纳米囊泡（即包封效率）是确保开发的纳米系统中药物适当包封的关键参数之一。有几个因素会影响药物的包封效率，因此需要进行多次评估，以确保药物保留，并降低成本和时间。监督机器学习可以构建算法，挖掘早期研究中可用的数据，以预测特定类型纳米颗粒的性能。利用多种回归算法来预测纳米材料（例如，脂囊泡）包封效率的比较研究很少。在此，我们报告了一种详细的方法，用于使用不同的回归算法（即 CatBoost、线性回归、支持向量回归和人工神经网络）预测纳米材料（例如脂囊泡）的包封效率，以选择表现最佳的模型来估计包封效率。研究结果表明，CatBoost 算法的表现最佳，最大 R 分数为 0.98，均方误差（<10）。在影响药物包封效率的各种参数中，CatBoost 模型的结果表明，药物与脂质的比例是影响包封效率的主要因素，其次是脂质与表面活性剂的摩尔比。因此，监督机器学习可应用于未来选择实现高药物包封效率的脂囊泡的成分，同时最小化实验程序和成本。
