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Wei W, Shuo F, Ye Z, et al. Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm[J].

Acta Pharmaceutica Sinica B, 2022, 12(6): 2950-2962. (IF: 14.90)

Jun L, Han G, Ye Z, et al. In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques[J].

Carbohydrate Polymers, 2022, 275: 118712.(IF: 10.72)

Wang W, Ye Z, Gao H, et al. Computational pharmaceutics - A new paradigm of drug delivery[J].

Journal of Controlled Release, 2021, 338: 119-136. (IF: 11.46)

Gao H, Jia H, Dong J, et al. Integrated in silico formulation design of self-emulsifying drug delivery systems[J].

Acta Pharmaceutica Sinica B, 2021, 11(11): 3585-3594. (IF: 14.90)

Ye Z, Defang Ouyang. Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms[J].

Journal of Cheminformatics, 98(2021).(IF: 8.26)

Wang N, Sun H, Dong J. PharmDE: A new expert system for drug-excipient compatibility evaluation[J].

International Journal of Pharmaceutics,2021, 607(120962). (IF=6.51)

Wang N, Sun H, Dong J. PharmSD: A novel AI-based computational platform for solid dispersion formulation design[J].

International Journal of Pharmaceutics,2021, 604(120705). (IF=6.51)

He Y, Ye Z, Liu X, et al. Can machine learning predict drug nanocrystals?[J].

Journal of Controlled Release, 2020, 322: 274-285. (IF: 11.46)

Han R, Xiong H, Ye Z, et al. Predicting physical stability of solid dispersions by machine learning techniques[J].

Journal of Controlled Release, 2019, 311: 16-25. (IF: 11.46)

Zhao Q, Ye Z, Su Y, et al. Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques[J].

Acta Pharmaceutica Sinica B, 2019, 9(6): 1241-1252. (IF: 14.90)

Gao H, Wang W, Dong J, et al. An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design[J].

European Journal of Pharmaceutics and Biopharmaceutics, 2020, 158: 336-346.(IF: 5.59)

Yang Y, Ye Z, Su Y, et al. Deep learning for in vitro prediction of pharmaceutical formulations[J].

Acta pharmaceutica sinica B, 2019, 9(1): 177-185. (IF: 14.90)

Ma Y, Zhong L, Peng Z, et al. Development of a Highly Water-Soluble Lycopene Cyclodextrin Ternary Formulation by the Integrated Experimental and Modeling Techniques[J].

AAPS PharmSciTech, 2021, 22(1): 1-9. (IF: 4.02)

Yang Y, Ye Z, Su Y, et al. Deep learning for in vitro prediction of pharmaceutical formulations[J].

Acta pharmaceutica sinica B, 2019, 9(1): 177-185.(IF: 14.90)

Gao H, Ye Z, Dong J, et al. Predicting drug/phospholipid complexation by the lightGBM method[J].

Chemical Physics Letters, 2020, 747: 137354. (IF: 2.72)

Ye Z, Yang Y, Li X, et al. An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction[J].

Molecular pharmaceutics, 2018, 16(2): 533-541.(IF: 5.36)

Han R, Yang Y, Li X, et al. Predicting oral disintegrating tablet formulations by neural network techniques[J].

Asian journal of pharmaceutical sciences, 2018, 13(4): 336-342.(IF: 9.27)