典型文献
Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization
文献摘要：
Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates,as it has a profound impact on the crystal-lization process.Solubility prediction,as an alternative to experiments which can reduce waste and improve crystal-lization process efficiency,has attracted increasing atten-tion.However,there are still many urgent challenges thus far.Herein we used seven descriptors based on under-standing 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.
文献关键词：
中图分类号：
作者姓名：
Yiming Ma;Zhenguo Gao;Peng Shi;Mingyang Chen;Songgu Wu;Chao Yang;Jing-Kang Wang;Jingcai Cheng;Junbo Gong
作者机构：
School of Chemical Engineering and Technology,State Key Laboratory of Chemical Engineering,Tianjin University,Tianjin 300072,China;The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin,Tianjin 300072,China;Key Laboratory of Green Process and Engineering,Institute of Process Engineering,Chinese Academy of Sciences,Beijing 100190,China
文献出处：
引用格式：
[1]Yiming Ma;Zhenguo Gao;Peng Shi;Mingyang Chen;Songgu Wu;Chao Yang;Jing-Kang Wang;Jingcai Cheng;Junbo Gong-.Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization)[J].化学科学与工程前沿,2022(04):523-535
A类：
B类：
Machine,learning,solubility,prediction,methodology,evaluation,active,pharmaceutical,ingredients,industrial,crystallization,Solubility,has,been,widely,regarded,fundamental,property,small,molecule,drugs,candidates,profound,impact,process,alternative,experiments,which,reduce,waste,improve,efficiency,attracted,increasing,atten,However,there,are,still,many,urgent,challenges,thus,far,Herein,used,seven,descriptors,under,standing,dissolution,behavior,establish,models,by,machine,algorithms,data,APIs,ethanol,were,considered,constructed,random,decision,forests,artificial,neural,network,optimized,structure,accuracy,Furthermore,comparison,traditional,methods,including,modified,equation,quantitative,relationships,carried,out,highest,shown,testing,set,proves,that,ML,have,best,ability,Multiple,linear,regression,stepwise,further,investigate,critical,determining,value,results,revealed,properties,solute,solvent,interaction,both,provide,nonnegligible,contribution
AB值：
0.61341
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