通过基于人工智能方法的数值模拟,优化超临界CO2系统内的药物溶解度
Meixiuli Li1, Wenyan Jiang2, Shuang Zhao1
1Department of Human Anatomy and Embryology, Pu Ai Medical School, Shaoyang University, Shaoyang, 422000, Hunan, China.
Scientific reports
|October 1, 2024
概括
机器学习模型准确地预测了超临界二氧化碳密度和尼酸溶解度. 巴纳克斯交配优化器有效调整了用于制药应用的模型.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 机器学习应用 机器学习应用
背景情况:
- 超临界二氧化碳 (SC-CO2) 是制药过程中一个有前途的溶剂.
- 准确预测SC-CO2密度和药物溶解度对于工艺设计至关重要.
- 机器学习为模拟复杂的物理性质提供了一种强大的方法.
研究的目的:
- 评估多项式回归 (PR),极端梯度提升 (XGB) 和LASSO模型来预测SC-CO2密度.
- 评估这些模型在估计尼弗酸在SC-CO2中的溶解度时的性能.
- 使用巴纳克尔斯交配优化器 (BMO) 来优化模型超参数.
主要方法:
- 使用了PR,XGB和LASSO回归模型.
- 使用巴纳克斯配对优化器 (BMO) 进行超参数调整.
- 使用R平方值用于密度和可溶性预测的验证模型性能.
主要成果:
- 对于SC-CO2密度 (R2=0.99207) 和尼酸溶解度 (R2=0.96949),PR获得了最高的准确性.
- 在密度 (R2=0.92673) 和溶解度 (R2=0.92961) 方面,XGB表现出强大的预测性能.
- 拉索提供了良好的预测能力,密度的R2值为0.81917,溶解度为0.82094.
结论:
- 机器学习模型,特别是PR和XGB,在预测SC-CO2密度和尼酸溶解度方面表现出高准确性.
- 在这种情况下,BMO算法对优化机器学习模型是有效的.
- 这些发现支持机器学习在制药行业用于溶剂-溶解物质性质估计的应用.
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