通过机器学习模型进行膜蒸的智能分析,用于制药分离
1Department of Pharmacology, College of Pharmacy, Shaqra University, Shaqra, 11961, Saudi Arabia. alkhammash@su.edu.sa.
Scientific reports
|October 2, 2024
概括
这项研究使用膜蒸模拟了制药分离,发现多层感知器 (MLP) 机器学习模型对预测溶液度非常准确. 谨慎的模型选择对于有效的膜工艺模拟至关重要.
科学领域:
- 环境工程 环境工程
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 膜蒸 (MD) 是制药分离的一个有前途的技术.
- 准确预测溶液度对于优化MD过程至关重要.
- 机器学习为模拟复杂的分离现象提供了先进的工具.
研究的目的:
- 通过膜蒸通过计算模拟药物分离.
- 评估多层感知子 (MLP),玛回归和支向量回归 (SVR) 模型在预测溶液度方面的有效性.
- 使用红鹿算法 (RDA) 来优化模型超参数.
主要方法:
- 在连续模式下进行膜蒸的计算模拟.
- 实施质量转移和机器学习模型.
- 专注于膜的料部分的溶液度.
- 通过红鹿算法 (RDA) 进行超参数优化.
主要成果:
- 多层感知器 (MLP) 模型显示出卓越的准确性 (R2=0.9955,MAE=0.0084,RMSE=0.0148).
- 玛回归显示了可接受的性能 (R2=0.9214),适用于偏斜的数据.
- 支持向量回归 (SVR) 的表现最低 (R2=0.8710),但捕获了一般趋势.
结论:
- 多层感知器是模拟膜蒸中药物分离的最有效的回归模型.
- 模型选择和超参数优化对于精确的膜过程计算分析至关重要.
- 机器学习与计算模拟相结合,增强了对制药分离技术的理解和优化.
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