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Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine leaing is increasingly advancing many aspects in mode society and has achieved significant success in multiple subjects.Current research demonstrated that machine leaing 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 leaing.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was 0.86.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 leaing model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine leaing 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 leaing in pharmaceutical formulation design.