通过机器学习方法对酸溶解度与溶剂组成,温度和压力的相关性进行高级分析
Wael A Mahdi1, Adel Alhowyan1, Ahmad J Obaidullah2
1Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia.
Scientific reports
|March 24, 2025
概括
机器学习可以准确地预测不同溶剂的酸溶解度. 卷积神经网络 (CNN) 展示了卓越的性能,强调了数据预处理和模型优化对溶解性预测的重要性.
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:
- 准确预测酸溶解度对于其在制药和化学过程中的应用至关重要.
- 传统的溶解度测定方法可能耗时且资源密集.
- 机器学习提供了一个强大的方法来建模和预测基于各种参数的可溶性.
研究的目的:
- 开发和评估用于预测酸溶解度的机器学习模型.
- 研究温度,压力和溶剂类型对溶解度的影响.
- 为了比较不同的机器学习算法在溶解性预测中的性能.
主要方法:
- 使用了217个数据点和15个输入特征的数据集,包括温度,压力和13种不同的溶剂.
- 数据的预处理涉及到最小-最大缩放以实现正常化和k-最近邻居异常检测 (KNNOD) 以消除异常值.
- 采用的模型包括卷积神经网络 (CNN),多项式回归 (PR) 和内核回归 (KRR),使用超带方法进行超参数优化.
主要成果:
- 卷积神经网络 (CNN) 获得了最高的准确性,R平方得分为0.989,平均平方误差 (MSE) 为4.161203E-05,平均绝对误差 (MAE) 为3.760119E-03.
- 核心回归 (KRR) 显示了0.913873.3的竞争R平方得分.
- 该研究表明,所选择的预处理技术和超参数调整在提高预测准确性的有效性.
结论:
- 机器学习,特别是CNN,提供了一个非常准确和高效的方法来预测酸溶解度.
- 这些发现强调了强大的数据预处理和模型选择对于可靠的可溶性预测的重要性.
- 这项研究为预测不同溶剂环境中的酸溶解度提供了宝贵的见解,有助于过程优化和开发.
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