利用基于序列模型的优化器集成机器学习模型,将famotidine在超临界二氧化碳中的可溶性相关
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.
这项研究使用机器学习来模拟famotidine在超临界二氧化碳中的可溶性. 四次多项式回归准确地预测了溶解度和密度,有助于纳米医学的发展.
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:
- 超临界二氧化碳 (sc-CO2) 是用于制药应用的可调节溶剂.
- 提高药物溶解度对于开发有效的纳米药物至关重要.
- 在sc-CO2中的法莫蒂丁 (FAM) 溶解性需要准确的预测模型.
研究的目的:
- 在不同温度和压力下,研究famotidine在sc-CO2中的可溶性.
- 开发和比较用于预测FAM溶解度和sc-CO2密度的机器学习模型.
- 为准确的溶解度和密度预测确定最佳模型.
主要方法:
- 使用二次多项式回归 (QPR),加权最小平方 (WLS) 和直角匹配追求 (OMP) 来进行数据分析.
- 利用基于模型的顺序优化 (SMBO) 进行超参数调整.
- 计算FAM溶解度和sc-CO2密度作为温度和压力的函数.
主要成果:
- QPR表现出卓越的性能,在FAM溶解性预测中获得0.95858的R2.
- 在QPR中,溶解度的MAPE (1.64278E+00) 和RMSE (9.6833E-02) 均较低.
- 在低误差指标的sc-CO2密度预测中,QPR获得了0.99733的R2.
结论:
- 该QPR模型准确地预测了sc-CO2中的famotidine可溶性.
- 在不同的温度和压力下,QPR对预测sc-CO2密度非常有效.
- 精确的建模有助于制备具有增强水溶性的纳米药物.
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