Zhuyifan Ye

1.3k total citations
19 papers, 908 citations indexed

About

Zhuyifan Ye is a scholar working on Computational Theory and Mathematics, Pharmaceutical Science and Molecular Biology. According to data from OpenAlex, Zhuyifan Ye has authored 19 papers receiving a total of 908 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computational Theory and Mathematics, 10 papers in Pharmaceutical Science and 9 papers in Molecular Biology. Recurrent topics in Zhuyifan Ye's work include Computational Drug Discovery Methods (12 papers), Analytical Chemistry and Chromatography (8 papers) and Drug Solubulity and Delivery Systems (8 papers). Zhuyifan Ye is often cited by papers focused on Computational Drug Discovery Methods (12 papers), Analytical Chemistry and Chromatography (8 papers) and Drug Solubulity and Delivery Systems (8 papers). Zhuyifan Ye collaborates with scholars based in Macao, China and United Kingdom. Zhuyifan Ye's co-authors include Defang Ouyang, Hanlu Gao, Wei Wang, Yilong Yang, Qianqian Zhao, Yan Su, Dongsheng Cao, Run Han, Jinzhong Lin and Xiaoshan Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, Advanced Drug Delivery Reviews and Chemical Engineering Journal.

In The Last Decade

Zhuyifan Ye

19 papers receiving 881 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhuyifan Ye Macao 15 303 302 231 199 178 19 908
Pauric Bannigan Canada 14 186 0.6× 144 0.5× 168 0.7× 134 0.7× 217 1.2× 25 756
G. K. Raju United States 5 257 0.8× 84 0.3× 114 0.5× 323 1.6× 159 0.9× 7 947
Shakhawath Hossain Sweden 12 286 0.9× 165 0.5× 90 0.4× 107 0.5× 111 0.6× 29 771
Hanlu Gao China 11 191 0.6× 92 0.3× 90 0.4× 97 0.5× 98 0.6× 22 528
M. Sherry Ku United States 12 174 0.6× 156 0.5× 169 0.7× 332 1.7× 71 0.4× 14 974
Fuzheng Ren China 15 181 0.6× 45 0.1× 156 0.7× 283 1.4× 141 0.8× 40 764
Xiangyu Ma United States 16 164 0.5× 46 0.2× 125 0.5× 421 2.1× 118 0.7× 34 888
Run Han Macao 10 102 0.3× 73 0.2× 102 0.4× 163 0.8× 105 0.6× 12 439
Howard Y. Ando United States 15 157 0.5× 144 0.5× 130 0.6× 109 0.5× 110 0.6× 29 580
William J. Lambert United States 21 361 1.2× 100 0.3× 72 0.3× 381 1.9× 399 2.2× 43 1.8k

Countries citing papers authored by Zhuyifan Ye

Since Specialization
Citations

This map shows the geographic impact of Zhuyifan Ye's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zhuyifan Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhuyifan Ye more than expected).

Fields of papers citing papers by Zhuyifan Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhuyifan Ye. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zhuyifan Ye. The network helps show where Zhuyifan Ye may publish in the future.

Co-authorship network of co-authors of Zhuyifan Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Zhuyifan Ye. A scholar is included among the top collaborators of Zhuyifan Ye based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zhuyifan Ye. Zhuyifan Ye is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Wang, Wei, Nannan Wang, Zhuyifan Ye, et al.. (2025). An Integrated AI‐PBPK Platform for Predicting Drug In Vivo Fate and Tissue Distribution in Human and Inter‐Species Extrapolation. Clinical Pharmacology & Therapeutics. 118(4). 865–875. 4 indexed citations
2.
Deng, Shiwei, Yi‐Yang Wu, Zhuyifan Ye, & Defang Ouyang. (2024). In silico prediction of metabolic stability for ester-containing molecules: Machine learning and quantum mechanical methods. Chemometrics and Intelligent Laboratory Systems. 257. 105292–105292. 2 indexed citations
3.
Wu, Zhenhua, Nannan Wang, Zhuyifan Ye, et al.. (2024). FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction. Molecular Pharmaceutics. 22(1). 330–342. 5 indexed citations
4.
Ye, Zhuyifan, Nannan Wang, Jiantao Zhou, & Defang Ouyang. (2024). Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks. The Innovation. 5(2). 100562–100562. 17 indexed citations
5.
Han, Run, Zhuyifan Ye, Yunsen Zhang, et al.. (2023). Predicting liposome formulations by the integrated machine learning and molecular modeling approaches. Asian Journal of Pharmaceutical Sciences. 18(3). 100811–100811. 31 indexed citations
6.
Wang, Nannan, Yunsen Zhang, Wei Wang, et al.. (2023). How can machine learning and multiscale modeling benefit ocular drug development?. Advanced Drug Delivery Reviews. 196. 114772–114772. 27 indexed citations
7.
Ye, Zhuyifan, Wenwen Zheng, Jian Chen, et al.. (2022). Machine learning in accelerating microsphere formulation development. Drug Delivery and Translational Research. 13(4). 966–982. 20 indexed citations
8.
Ye, Zhuyifan, Yuchen Feng, Lei Jin, et al.. (2022). Development of in silico methodology for siRNA lipid nanoparticle formulations. Chemical Engineering Journal. 442. 136310–136310. 32 indexed citations
9.
Wang, Wei, et al.. (2021). Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm. Acta Pharmaceutica Sinica B. 12(6). 2950–2962. 100 indexed citations
10.
Li, Junjun, et al.. (2021). In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques. Carbohydrate Polymers. 275. 118712–118712. 26 indexed citations
11.
Wang, Wei, Zhuyifan Ye, Hanlu Gao, & Defang Ouyang. (2021). Computational pharmaceutics - A new paradigm of drug delivery. Journal of Controlled Release. 338. 119–136. 137 indexed citations
12.
Ye, Zhuyifan & Defang Ouyang. (2021). Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms. Journal of Cheminformatics. 13(1). 98–98. 55 indexed citations
13.
Ye, Zhuyifan, Wenmian Yang, Yilong Yang, & Defang Ouyang. (2021). Interpretable machine learning methods for in vitro pharmaceutical formulation development. SHILAP Revista de lepidopterología. 2(2). 195–207. 14 indexed citations
14.
Gao, Hanlu, Wei Wang, Jie Dong, Zhuyifan Ye, & Defang Ouyang. (2020). An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design. European Journal of Pharmaceutics and Biopharmaceutics. 158. 336–346. 50 indexed citations
15.
He, Yuan, Zhuyifan Ye, Xinyang Liu, et al.. (2020). Can machine learning predict drug nanocrystals?. Journal of Controlled Release. 322. 274–285. 77 indexed citations
16.
Ye, Zhuyifan, Jie Dong, Hanlu Gao, et al.. (2020). Predicting drug/phospholipid complexation by the lightGBM method. Chemical Physics Letters. 747. 137354–137354. 32 indexed citations
17.
Zhao, Qianqian, Zhuyifan Ye, Yan Su, & Defang Ouyang. (2019). Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques. Acta Pharmaceutica Sinica B. 9(6). 1241–1252. 81 indexed citations
18.
Han, Run, Hui Xiong, Zhuyifan Ye, et al.. (2019). Predicting physical stability of solid dispersions by machine learning techniques. Journal of Controlled Release. 311-312. 16–25. 120 indexed citations
19.
Ye, Zhuyifan, Yilong Yang, Xiaoshan Li, Dongsheng Cao, & Defang Ouyang. (2018). An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction. Molecular Pharmaceutics. 16(2). 533–541. 78 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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