Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization
文献类型：期刊论文
| 作者 | Ma, Yiming1,2; Gao, Zhenguo1,2; Shi, Peng1,2; Chen, Mingyang1,2; Wu, Songgu1,2; Yang, Chao3; Wang, Jingkang1,2; Cheng, Jingcai3; Gong, Junbo1,2 |
| 刊名 | FRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING
 |
| 出版日期 | 2021-10-13 |
| 页码 | 13 |
| ISSN号 | 2095-0179 |
| 关键词 | solubility prediction machine learning artificial neural network random decision forests |
| DOI | 10.1007/s11705-021-2083-5 |
| 英文摘要 | 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. |
| WOS关键词 | BINARY SOLVENT MIXTURES ; WATER PLUS METHANOL ; THERMODYNAMIC ANALYSIS ; AQUEOUS SOLUBILITY ; DRUG SOLUBILITY ; SELECTION ; BEHAVIOR ; ETHANOL |
| 资助项目 | National Natural Science Foundation of China[21938009] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:000706926000001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/50603]  |
| 专题 | 中国科学院过程工程研究所 |
| 通讯作者 | Cheng, Jingcai; Gong, Junbo |
| 作者单位 | 1.Coinnovat Ctr Chem & Chem Engn Tianjin, Tianjin 300072, Peoples R China 2.Tianjin Univ, Sch Chem Engn & Technol, State Key Lab Chem Engn, Tianjin 300072, Peoples R China 3.Chinese Acad Sci, Inst Proc Engn, Key Lab Green Proc & Engn, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Yiming,Gao, Zhenguo,Shi, Peng,et al. Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization[J]. FRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING,2021:13. |
| APA | Ma, Yiming.,Gao, Zhenguo.,Shi, Peng.,Chen, Mingyang.,Wu, Songgu.,...&Gong, Junbo.(2021).Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization.FRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING,13. |
| MLA | Ma, Yiming,et al."Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization".FRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING (2021):13. |
入库方式： OAI收割
来源：过程工程研究所
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