使用人工智能技术在溶剂中药物溶解性的高级建模:对纳米化处理药物候选物的评估
Turki Al Hagbani1, Sameer Alshehri2, Sami Bawazeer3
1Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Frontiers in medicine
|August 6, 2024
概括
断片多项式回归 (PPR) 最好预测二氧化碳在超临界的溶解度,优于内核回归 (KRR) 和Tweedie回归 (TDR). 与水循环算法 (WCA) 进行超参数调整,提高了这个关键制药过程的模型准确性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
- 物理化学 物理化学
背景情况:
- 超临界二氧化碳 (scCO2) 是一种有希望的绿色溶剂,用于药物溶解性预测.
- 准确预测烯在scCO2中的溶解度对于制药配方至关重要.
- 开发强大的预测模型需要了解温度和压力的影响.
研究的目的:
- 分析和比较多重回归模型的性能,以预测烯在scCO2中的溶解度.
- 用水循环算法 (WCA) 评估超参数调整对模型准确性的影响.
- 为了确定这个特定应用程序最有效的回归模型.
主要方法:
- 使用了零碎多项式回归 (PPR),内核回归 (KRR) 和tweeedie回归 (TDR) 模型.
- 与温度 (T) 和压力 (P) 作为输入变量相关的烯溶解度.
- 采用水循环算法 (WCA) 进行回归模型的超参数优化.
主要成果:
- 该PPR模型实现了最高的性能,R2为0.97111,MSE为1.6867E-09和MAE为3.01040E-05.
- 该KRR模型表现良好 (R2 = 0.95044,MSE = 2.5499E-09,MAE = 3.49707E-05).该模型的性能很好.
- 在TDR模型中,准确度较低 (R2 = 0.84413,MSE = 7.4249E-09,MAE = 5.69159E-05).
结论:
- 优化的PPR模型显示了对scCO2中的烯溶解度的卓越预测能力.
- 通过WCA进行超参数调整可以显著提高溶解度预测模型的准确性.
- 这些发现支持使用先进的回归技术来优化制药过程.
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