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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization
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
2021-10-13
Source PublicationFRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING
ISSN2095-0179
Pages13
AbstractSolubility 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.
Keywordsolubility prediction machine learning artificial neural network random decision forests
DOI10.1007/s11705-021-2083-5
Language英语
WOS KeywordBINARY SOLVENT MIXTURES ; WATER PLUS METHANOL ; THERMODYNAMIC ANALYSIS ; AQUEOUS SOLUBILITY ; DRUG SOLUBILITY ; SELECTION ; BEHAVIOR ; ETHANOL
Funding ProjectNational Natural Science Foundation of China[21938009]
WOS Research AreaEngineering
WOS SubjectEngineering, Chemical
Funding OrganizationNational Natural Science Foundation of China
WOS IDWOS:000706926000001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/50604
Collection中国科学院过程工程研究所
Corresponding AuthorCheng, Jingcai; Gong, Junbo
Affiliation1.Tianjin Univ, Sch Chem Engn & Technol, State Key Lab Chem Engn, Tianjin 300072, Peoples R China
2.Coinnovat Ctr Chem & Chem Engn Tianjin, Tianjin 300072, Peoples R China
3.Chinese Acad Sci, Inst Proc Engn, Key Lab Green Proc & Engn, Beijing 100190, Peoples R China
Recommended Citation
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.
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