Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques  

作　　者：Qianqian Zhao Zhuyifan Ye Yan Su Defang Ouyang 

机构地区：[1]State Key Laboratory of Quality Research in Chinese Medicine,Institute of Chinese Medical Sciences(ICMS),University of Macao,Macao,China

出　　处：《Acta Pharmaceutica Sinica B》2019年第6期1241-1252,共12页药学学报（英文版）

基　　金：supported by the University of Macao Research Grants(MYRG2016-00038ICMS-QRCM and MYRG2016-00040-ICMS-QRCM,Macao,China).

摘　　要：Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects.Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins(CDs)systems.Molecular descriptors of compounds and experimental conditions were employed as inputs,while complexation free energy as outputs.Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was0.86.The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems.In the specific ketoprofen-CD systems,machine learning model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations.In conclusion,the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems.Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects.Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins (CDs) systems.Molecular descriptors of compounds and experimental conditions were employed as inputs,while complexation free energy as outputs.Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was0.86.The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems.In the specific ketoprofen-CD systems,machine learning model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations.In conclusion,the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems.Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.

关 键 词：Machine LEARNING Deep LEARNING LightGBM Random FOREST CYCLODEXTRIN BINDING free energy Molecular modeling KETOPROFEN 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R943[自动化与计算机技术—控制科学与工程]