Tasqeeruddin S, Sultana Shaheen, Alsayari Abdulrhman
Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, 62529, Saudi Arabia.
Department of Pharmacology, Anwarul Uloom College of Pharmacy, Hyderabad, 500001, India.
Sci Rep. 2025 Mar 12;15(1):8453. doi: 10.1038/s41598-025-93596-z.
This study investigates the utilization of three regression models, i.e., Kernel Ridge Regression (KRR), nu-Support Vector Regression ([Formula: see text]-SVR), and Polynomial Regression (PR) for the purpose of forecasting the concentration (C) of a drug within a specified environment, relying on the coordinates (x and y). The analyses were carried out for separation of drug from a solution by adsorption process where the concentration of drug was obtained in the solution and the adsorbent via computational fluid dynamics (CFD), and the results of concentration distribution were used or machine learning modeling. The model considered mass transfer and fluid flow equations to determine concentration distribution of solute in the system. The hyperparameter optimization was carried out using the Fruit-Fly Optimization Algorithm (FFOA), a nature-inspired optimization technique. Our results demonstrate the performance of each model in terms of key regression metrics. KRR achieved an R score of 0.84851, with a Root Mean Square Error (RMSE) of 1.0384E-01 and a Mean Absolute Error (MAE) of 7.27762E-02. [Formula: see text]-SVR exhibited exceptional accuracy with an R of 0.98593, accompanied by an RMSE of 3.5616E-02 and an MAE of 1.36749E-02. PR, a traditional regression method, attained an R score of 0.94077, an RMSE of 7.2042E-02, and an MAE of 4.81533E-02.
本研究调查了三种回归模型的应用，即核岭回归（KRR）、ν-支持向量回归（ν-SVR）和多项式回归（PR），目的是根据坐标（x和y）预测特定环境中药物的浓度（C）。分析是针对通过吸附过程从溶液中分离药物进行的，其中通过计算流体动力学（CFD）获得溶液和吸附剂中药物的浓度，并将浓度分布结果用于机器学习建模。该模型考虑了传质和流体流动方程来确定系统中溶质的浓度分布。使用果蝇优化算法（FFOA）进行超参数优化，这是一种受自然启发的优化技术。我们的结果展示了每个模型在关键回归指标方面的性能。KRR的R分数为0.84851，均方根误差（RMSE）为1.0384E-01，平均绝对误差（MAE）为7.27762E-02。ν-SVR表现出卓越的准确性，R为0.98593，RMSE为3.5616E-02，MAE为1.36749E-02。PR作为一种传统回归方法，R分数为0.94077，RMSE为7.2042E-02，MAE为4.81533E-02。
