Chang‐Yu Hsieh

8.1k total citations · 4 hit papers
128 papers, 5.0k citations indexed

About

Chang‐Yu Hsieh is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Chang‐Yu Hsieh has authored 128 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Computational Theory and Mathematics, 63 papers in Molecular Biology and 52 papers in Materials Chemistry. Recurrent topics in Chang‐Yu Hsieh's work include Computational Drug Discovery Methods (67 papers), Machine Learning in Materials Science (47 papers) and Protein Structure and Dynamics (29 papers). Chang‐Yu Hsieh is often cited by papers focused on Computational Drug Discovery Methods (67 papers), Machine Learning in Materials Science (47 papers) and Protein Structure and Dynamics (29 papers). Chang‐Yu Hsieh collaborates with scholars based in China, United States and Macao. Chang‐Yu Hsieh's co-authors include Tingjun Hou, Dongsheng Cao, Zhenhua Wu, Jiacai Yi, Chengkun Wu, Zhijiang Yang, Li Fu, Aiping Lü, Xiangxiang Zeng and Xiang Chen and has published in prestigious journals such as Chemical Reviews, Journal of the American Chemical Society and Physical Review Letters.

In The Last Decade

Chang‐Yu Hsieh

121 papers receiving 4.9k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

2021 1.7k citations Zhenhua Wu, Chang‐Yu Hsieh et al. profile →
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

2021 388 citations Dejun Jiang, Zhenhua Wu et al. profile →
Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model

2025 33 citations Jike Wang, Yu Kang et al. Science Advances profile →
AI‐Guided Design of Antimicrobial Peptide Hydrogels for Precise Treatment of Drug‐resistant Bacterial Infections

2025 27 citations Jike Wang, Chang‐Yu Hsieh et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chang‐Yu Hsieh China	29	2.3k	2.1k	1.2k	678	574	128	5.0k
John B. O. Mitchell United Kingdom	39	2.4k 1.0×	3.1k 1.5×	1.8k 1.5×	163 0.2×	747 1.3×	124	5.8k
Gregory A. Landrum Switzerland	32	1.9k 0.8×	1.7k 0.8×	1.6k 1.4×	200 0.3×	693 1.2×	69	4.6k
Ola Engkvist Sweden	43	5.3k 2.3×	4.2k 2.0×	3.5k 3.0×	536 0.8×	590 1.0×	166	8.5k
Andrew J. Doig United Kingdom	42	1.6k 0.7×	5.5k 2.6×	1.1k 0.9×	296 0.4×	606 1.1×	108	8.6k
Dimitris K. Agrafiotis United Kingdom	33	2.0k 0.9×	1.6k 0.8×	642 0.5×	327 0.5×	306 0.5×	169	4.4k
Huanxiang Liu China	42	2.1k 0.9×	3.1k 1.5×	1.0k 0.9×	136 0.2×	944 1.6×	307	6.7k
John L. Klepeis United States	24	1.8k 0.8×	7.0k 3.3×	1.9k 1.6×	213 0.3×	970 1.7×	33	10.6k
Gianni De Fabritiis Spain	40	2.3k 1.0×	5.4k 2.6×	2.5k 2.1×	150 0.2×	388 0.7×	119	7.9k
Paul Czodrowski Germany	21	1.3k 0.6×	2.5k 1.2×	738 0.6×	228 0.3×	318 0.6×	41	4.2k
Jonathan D. Hirst United Kingdom	41	860 0.4×	3.1k 1.5×	1.0k 0.9×	269 0.4×	549 1.0×	186	5.3k

Countries citing papers authored by Chang‐Yu Hsieh

Since Specialization

Citations

This map shows the geographic impact of Chang‐Yu Hsieh'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 Chang‐Yu Hsieh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chang‐Yu Hsieh more than expected).

Fields of papers citing papers by Chang‐Yu Hsieh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chang‐Yu Hsieh. 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 Chang‐Yu Hsieh. The network helps show where Chang‐Yu Hsieh may publish in the future.

Co-authorship network of co-authors of Chang‐Yu Hsieh

This figure shows the co-authorship network connecting the top 25 collaborators of Chang‐Yu Hsieh. A scholar is included among the top collaborators of Chang‐Yu Hsieh 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 Chang‐Yu Hsieh. Chang‐Yu Hsieh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liu, Yifei, Yiheng Zhu, Jike Wang, et al.. (2025). A Multi‐Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search. Advanced Science. 12(20). e2410640–e2410640. 4 indexed citations

Wang, Jike, Lei Jiang, Hui Zhang, et al.. (2025). Robust protein–ligand interaction modeling through integrating physical laws and geometric knowledge for absolute binding free energy calculation. Chemical Science. 16(12). 5043–5057. 3 indexed citations

Wang, Jike, Yu Kang, Peichen Pan, et al.. (2025). Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model. Science Advances. 11(10). eads8932–eads8932. 33 indexed citations breakdown →

Shen, Chao, Xujun Zhang, Odin Zhang, et al.. (2025). Unlocking the application potential of AlphaFold3-like approaches in virtual screening. Chemical Science. 17(5). 2858–2879.

Zhu, Yuchen, Xiaohong Chen, Yun Huang, et al.. (2025). DGMM: A Deep Learning-Genetic Algorithm Framework for Efficient Lead Optimization in Drug Discovery. Journal of Chemical Information and Modeling. 65(15). 8168–8180.

Wu, Jialu, et al.. (2025). Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation. Nature Communications. 16(1). 413–413. 3 indexed citations

Hsieh, Chang‐Yu, et al.. (2024). Neural-network-encoded variational quantum algorithms. Physical Review Applied. 21(1). 7 indexed citations

Zhu, Yiheng, Yixuan Wu, Jialu Wu, et al.. (2024). Multi-Modal CLIP-Informed Protein Editing. SHILAP Revista de lepidopterología. 4. 211–211. 2 indexed citations

Zhang, Odin, Yufei Huang, Xujun Zhang, et al.. (2024). FragGen: towards 3D geometry reliable fragment-based molecular generation. Chemical Science. 15(46). 19452–19465. 2 indexed citations

10.

Chen, Yu, Xinda Zhao, Chao Shen, et al.. (2024). Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language. Chemical Science. 15(34). 13727–13740. 5 indexed citations

11.

Li, Shuai, Jike Wang, Odin Zhang, et al.. (2024). ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning. Nature Communications. 15(1). 10127–10127. 10 indexed citations

12.

Zhang, Xujun, Odin Zhang, Chao Shen, et al.. (2023). Efficient and accurate large library ligand docking with KarmaDock. Nature Computational Science. 3(9). 789–804. 80 indexed citations

13.

Zhang, Shi‐Xin, Jonathan Allcock, Shuo Liu, et al.. (2023). TensorCircuit: a Quantum Software Framework for the NISQ Era. Quantum. 7. 912–912. 71 indexed citations

14.

Zhang, Shi‐Xin, Shi‐Xin Zhang, Chang‐Yu Hsieh, et al.. (2023). Variational Quantum‐Neural Hybrid Error Mitigation. Advanced Quantum Technologies. 6(10). 8 indexed citations

15.

Yang, Ziyi, Jiezhong Qiu, Danyu Li, et al.. (2023). A mutation-induced drug resistance database (MdrDB). Communications Chemistry. 6(1). 123–123. 7 indexed citations

16.

Shen, Chao, Xujun Zhang, Tong Chen, et al.. (2023). CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training. Chemical Science. 15(4). 1449–1471. 29 indexed citations

17.

Chen, Yuqin, Yu Chen, Chee‐Kong Lee, Shengyu Zhang, & Chang‐Yu Hsieh. (2022). Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks. Nature Machine Intelligence. 4(3). 269–278. 25 indexed citations

18.

Zhang, Shi‐Xin, Chang‐Yu Hsieh, Shengyu Zhang, & Hong Yao. (2022). Differentiable quantum architecture search. Quantum Science and Technology. 7(4). 45023–45023. 86 indexed citations

19.

Li, Yuquan, Chang‐Yu Hsieh, Xiaoqing Gong, et al.. (2022). An adaptive graph learning method for automated molecular interactions and properties predictions. Nature Machine Intelligence. 4(7). 645–651. 34 indexed citations

20.

Xu, Yi, Chang‐Yu Hsieh, Lin Wu, & L. K. Ang. (2018). Two-dimensional transition metal dichalcogenides mediated long range surface plasmon resonance biosensors. Journal of Physics D Applied Physics. 52(6). 65101–65101. 87 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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