实验和ALO优化的机器学习可解释模型用于药物吸附在原始顿石上的药物吸附.
Amina Bouaichaoui1, Nabila Boucherit1,2, Mohamed Kouider Amar3
1Laboratory of BioMaterials and Transport Phenomena (LBMPT), University Yahia Fares of Medea, 26000, Medea, Algeria.
Environmental geochemistry and health
|January 31, 2026
概括
原始托尼特有效地从水中去除了诸如基托芬烟酸盐,多西环素酸盐和尼斯塔丁等药品. 一个可解释的机器学习模型将分子结构与高吸附能力联系起来,为制药污染提供了可持续的解决方案.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 水生系统中的药物是持续的环境污染物.
- 传统的废水处理方法难以完全去除这些生物活性化合物.
研究的目的:
- 通过使用原始顿石 (RB) 来研究基托提芬烟酸 (KF),多环酸盐 (DXC) 和尼斯 (Nyst) 的吸附性去除.
- 通过批量实验和机器学习来评估吸附动力学,平衡和热力学.
- 开发一个可解释的ML模型,预测吸附能力,并将分子结构与去除效率联系起来.
主要方法:
- 用RB进行KF,DXC和Nyst.的批量吸附实验.
- 用同热,动力和热力学模型分析了吸附数据.
- 四个由狮优化器 (ALO) 优化的机器学习模型 (ANN,SVR,RF,XGBoost) 被训练来预测吸附.
- 用SHAP (夏普利添加式扩展) 分析来确定模型的可解释性.
主要成果:
- RB 显示出高吸附能力: 178.86 mg/g (KF), 222.91 mg/g (DXC) 和 190.25 mg/g (Nyst). 在这种情况下,RB 具有高吸附能力.
- 弗洛伊德利希等温是最好的平衡描述,表明多层吸附在异质表面.
- XGBoost模型实现了高精度 (R2=0.972),SHAP分析确定了吸附剂剂量,初始度,pH和分子大小 (nC) 作为关键因素.
- 吸附机制涉及静电吸引,键,阴离子交换和范德瓦尔斯力.
结论:
- 原始托尼特是一种具有成本效益和可扩展的吸附剂,用于制药清除.
- 可解释的ML框架成功将制药分子结构与吸附行为联系起来.
- 这种方法为了解和减轻水生环境中制药污染提供了一个有希望的战略.
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