机器学习估计和优化用于评估药品在超临界二氧化碳中的可溶性,以提高药物疗效
Hadil Faris Alotaibi1, Chou-Yi Hsu2, Fadhil Faez Sead3,4
1Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman University, Riyadh, 11671, Saudi Arabia. Hfalotaibi@pnu.edu.sa.
Scientific reports
|October 14, 2025
概括
这项研究使用机器学习预测了可溶性和溶剂密度. 优化的模型提高了药物的生物可用性,量子梯度增强和额外树木显示出卓越的性能.
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
- 药理动力学 药理动力学
背景情况:
- 超临界流体技术对于药物生产至关重要,需要精确控制溶解度和密度.
- 理论研究对于优化超临界过程以提高药物疗效至关重要.
研究的目的:
- 开发和验证用于预测可溶性和溶剂密度的机器学习模型.
- 优化超临界药物生产过程,以提高生物可用性.
主要方法:
- 开发了使用组合技术 (额外树木,随机森林,梯度增强,量子梯度增强) 的两输入,两输出机器学习模型.
- 使用鱼优化算法 (WOA) 调整模型超参数和决策树参数.
- 经过验证的模型与实验数据对胺醇溶解度和溶剂密度的验证.
主要成果:
- 量子梯度提升模型实现了0.985的R2,用于预测可溶性.
- 额外的树木模型实现了0.997的R2预测溶剂密度.
- 优化模型在预测关键过程参数方面表现出高准确度.
结论:
- 开发的机器学习模型准确地预测了超临界过程中的可溶性和溶剂密度.
- 优化的超临界技术可以提高药物的生物可用性.
- 该研究为过程可行性评估和优化提供了一个理论框架.
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