在超临界处理中对氨酸药物溶解度和溶剂密度的计算智能建模:梯度增强,额外的树木和随机森林模型
Mohammed Ghazwani1, M Yasmin Begum2
1Department of Pharmaceutics, College of Pharmacy, King Khalid University, P.O. Box 1882, 61441, Abha, Saudi Arabia.
Scientific reports
|June 21, 2023
概括
梯度提升和额外树木模型使用压力和温度准确预测氨酸溶解度和溶剂密度. 这些以树为基础的模型为化学化合物性质的确定提供了有效的方法.
科学领域:
- 计算化学是一种计算化学.
- 化学工程是化学工程的组成部分.
背景情况:
- 预测药物溶解度和溶剂密度对于工艺设计至关重要.
- 准确的属性预测可以减少实验成本和开发时间.
研究的目的:
- 评估基于树的模型的性能,以预测氨酸溶解度和溶剂密度.
- 为了建立溶解度,密度和输入参数 (如压力和温度) 之间的相关性.
主要方法:
- 使用梯度提升,额外的树木和随机森林模型.
- 训练有素模型对氨酸溶解度和密度数据进行训练.
- 使用WCA算法优化模型,并使用R2,MSE,MAPE和Max Error指标评估准确性.
主要成果:
- 梯度提升和额外树木模型实现了高精度 (R2 > 0.96).
- 两种模型都显示了低的平均绝对百分比误差 (MAPE) 和溶解度和密度的最大误差.
- 与梯度增强和额外树木相比,随机森林模型的预测准确性较低.
结论:
- 基于树的模型,特别是渐变增强和额外树,对于预测化学化合物的溶解度和密度是有效的.
- 这些模型可用于估计基于压力和温度的药物溶解度和密度,有助于早期过程设计.
相关概念视频
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