4.5 Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

期刊

出版社

SPRINGER
DOI: 10.1007/s11705-021-2083-5

关键词

solubility prediction; machine learning; artificial neural network; random decision forests

资金

  1. National Natural Science Foundation of China [21938009]

向作者/读者索取更多资源

Two solubility prediction models were established using machine learning algorithms, with a focus on API properties and solute-solvent interactions as critical factors. The models showed the best prediction ability compared to traditional methods, indicating the importance of understanding dissolution behavior in predicting solubility.
Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates, as it has a profound impact on the crystallization process. Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallization process efficiency, has attracted increasing attention. However, there are still many urgent challenges thus far. Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms. The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy. Furthermore, a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out. The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability. Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value. The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据