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Development of in silico methodology for siRNA lipid nanoparticle formulations Journal article
Chemical Engineering Journal, 2022,Volume: 442
Authors:  Gao, Haoshi;  Kan, Stanislav;  Ye, Zhuyifan;  Feng, Yuchen;  Jin, Lei;  Zhang, Xudong;  Deng, Jiayin;  Chan, Ging;  Hu, Yuanjia;  Wang, Yongjun;  Cao, Dongsheng;  Ji, Yuanhui;  Liang, Mingtao;  Li, Haifeng;  Ouyang, Defang
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2022/05/13
Cationic Lipids  Knockdown Efficiency  Lipid Nanoparticle  Machine Learning  Molecular Dynamic Simulation  Sirna  
Integrated in Silico Formulation Design of Lipid-based Drug Delivery Systems Thesis
University of Macau: University of Macau, 2022
Authors:  Haoshi Gao;  Li HF(李海峰);  Defang Ouyang
Adobe PDF | Favorite |  | Submit date:2022/08/15
Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm Journal article
Acta Pharmaceutica Sinica B, 2022
Authors:  Wang, Wei;  Feng, Shuo;  Ye, Zhuyifan;  Gao, Hanlu;  Lin, Jinzhong;  Ouyang, Defang
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2022/05/17
Formulation Prediction  Ionizable Lipid  Lightgbm  Lipid Nanoparticle  Machine Learning  Molecular Modeling  Mrna  Vaccine  
Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms Journal article
Journal of Cheminformatics, 2021,Volume: 13,Issue: 1
Authors:  Ye, Zhuyifan;  Ouyang, Defang
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2022/01/14
Deep Learning  Lightgbm  Machine Learning  Organic Solvents  Qspr  Solubility Prediction  
Integrated computer-aided formulation design: A case study of andrographolide/ cyclodextrin ternary formulation Journal article
Asian Journal of Pharmaceutical Sciences, 2021,Page: XXX-XXX
Authors:  Gao, H.;  Su, Y;  Wang, W.;  Xiong, W.;  Sun, X.;  Ji, Y.;  Yu, H.;  Li, H.-F.;  Ouyang, D.
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computer-aided  formulation design  andrographolide/cyclodextrin  
Interpretable machine learning methods for in vitro pharmaceutical formulation development Journal article
Food Frontiers, 2021,Volume: 2,Issue: 2,Page: 195-207
Authors:  Ye, Zhuyifan;  Yang, Wenmian;  Yang, Yilong;  Ouyang, Defang
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deep neural networks  formulation design  interpretable machine learning methods  the attention mechanism  
Integrated computer-aided formulation design: A case study of andrographolide/ cyclodextrin ternary formulation Journal article
Asian Journal of Pharmaceutical Sciences, 2021,Volume: 16,Issue: 4,Page: 494-507
Authors:  Gao, Haoshi;  Su, Yan;  Wang, Wei;  Xiong, Wei;  Sun, Xiyang;  Ji, Yuanhui;  Yu, Hua;  Li, Haifeng;  Ouyang, Defang
Favorite |  | TC[WOS]:2 TC[Scopus]:5 | Submit date:2021/10/02
Andrographolide  Cyclodextrins  Integrated Computer-aided Formulation Design  Machine Learning  Molecular Dynamic Simulation  Physiologically Based Absorption Modeling  
Can machine learning predict drug nanocrystals? Journal article
Journal of Controlled Release, 2020,Volume: 322,Page: 274-285
Authors:  He,Yuan;  Ye,Zhuyifan;  Liu,Xinyang;  Wei,Zhengjie;  Qiu,Fen;  Li,Hai Feng;  Zheng,Ying;  Ouyang,Defang
Favorite |  | TC[WOS]:18 TC[Scopus]:22 | Submit date:2021/03/01
Machine Learning  Nanocrystals  Particle Size  Polydispersity Index (Pdi)  Prediction  
Can machine learning predict drug nanocrystals? Journal article
Journal of Controlled Release, 2020,Page: 274-285
Authors:  He, Y.;  Ye, Z.;  Liu, X.;  Wei, Z.;  Qiu, F.;  Li, H.;  Zheng, Y.Y.;  Ouyang, D.
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2022/08/11
Machine Learningnanocrystalsparticle Sizepolydispersity Index (Pdi)Prediction  
Predicting drug/phospholipid complexation by the lightGBM method Journal article
Chemical Physics Letters, 2020,Volume: 747
Authors:  Gao,Haoshi;  Ye,Zhuyifan;  Dong,Jie;  Gao,Hanlu;  Yu,Hua;  Li,Haifeng;  Ouyang,Defang
Favorite |  | TC[WOS]:13 TC[Scopus]:14 | Submit date:2021/03/02