在不同的单溶剂中模拟和模拟本齐米达溶解行为,使用机器学习和热力学模型
Kassem Al Attabi1, Farag M A Altalbawy2, Deepak J3
1Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq; Department of computers Techniques engineering, College of technical engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq; Department of computers Techniques engineering, College of technical engineering, The Islamic University of Babylon, Babylon, Iraq.
Journal of pharmaceutical sciences
|December 22, 2025
概括
这项研究使用机器学习模拟了西米达在19种溶剂中的溶解度. 适应性增强证明了卓越的预测准确性,为可溶性预测的实验方法提供了具有成本效益的替代方案.
科学领域:
- 物理化学 物理化学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 西米达具有独特的物理化学特性和多样化的应用.
- 了解本齐米达的溶解性对于其配方和利用至关重要.
- 准确的可溶性预测有助于优化化学过程并降低实验成本.
研究的目的:
- 模拟和预测本齐米达在各种单溶剂中的可溶性.
- 评估不同机器学习算法和热力学模型的性能,以预测溶解度.
- 通过敏感性分析确定影响本齐米达溶解性的关键因素.
主要方法:
- 利用了171个实验性溶解点的数据集,跨越19个单溶剂.
- 使用的机器学习算法:K-最近邻居 (KNN),集体学习 (EL),随机森林,决策树和自适应增强.
- 应用热力学模型:阿佩尔布拉特, λh,NRTL,和马古尔斯.
- 使用蒙特卡洛模拟进行了灵敏度分析.
主要成果:
- 适应性增强实现了最高的预测准确性 (R2值) 和最低的错误指标 (RMSE,AARE).
- 单溶剂类型被确定为影响溶解度的最有影响的因素,其次是温度和单溶剂分子质量.
- 机器学习框架提供了强大的和验证的预测.
结论:
- 机器学习,特别是适应性增强,对于准确预测西醇溶解度是非常有效的.
- 开发的模型为实验性可溶性确定提供了经济和有效的补充.
- 这种方法有助于快速预测可溶性行为,支持过程开发.
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