在超临界二氧化碳绿色溶剂中预测药物溶解度,使用基于热力学特性的机器学习模型
Amir Hossein Sheikhshoaei1, Gholamhossein Sodeifian2,3,4
1Petroleum and Petrochemical Engineering School, Hakim Sabzevari University, Sabzevar, Iran.
Scientific reports
|November 17, 2025
概括
机器学习准确地预测药物在超临界二氧化碳 (scCO2) 中的溶解性. XGBoost模型表现出卓越的性能,为制药工艺设计的实验方法提供了可靠和高效的替代方案.
科学领域:
- 制药科学 制药科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 在超临界二氧化碳 (scCO2) 中准确的药物溶解性预测对于粒子工程和提取等制药过程至关重要.
- 实验性溶解性确定是资源密集型的,需要先进的预测方法.
研究的目的:
- 开发和评估用于预测scCO2中的药物溶解性的机器学习模型.
- 确定超出训练数据范围的可溶性预测最可靠,最准确的模型.
主要方法:
- 使用了机器学习算法,包括CatBoost,XGBoost,LightGBM和随机森林 (RF).
- 训练并测试了68种药物在scCO2中的可溶性数据的模型.
- 进行统计错误分析和图形评估,以比较模型性能.
主要成果:
- XGBoost模型表现出最高的准确性和可靠性,优于其他评估的模型.
- XGBoost实现了0.0605的低根平均平方误差 (RMSE) 和0.9984.4的高R2值.
- 97.68%的数据点属于XGBoost模型的适用范围,证实了它的预测稳定性.
结论:
- 在XGBoost算法提供了一个强大的和有效的方法来估计药物溶解性在scCO2.
- 机器学习模型,特别是XGBoost,在可溶性预测的传统方法上提供了显著的优势.
- 这种方法促进了高效的制药工艺设计和开发.
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