Oufan Zhang

938 total citations · 2 hit papers
16 papers, 591 citations indexed

About

Oufan Zhang is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Oufan Zhang has authored 16 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Materials Chemistry, 11 papers in Molecular Biology and 6 papers in Computational Theory and Mathematics. Recurrent topics in Oufan Zhang's work include Protein Structure and Dynamics (11 papers), Enzyme Structure and Function (8 papers) and Machine Learning in Materials Science (6 papers). Oufan Zhang is often cited by papers focused on Protein Structure and Dynamics (11 papers), Enzyme Structure and Function (8 papers) and Machine Learning in Materials Science (6 papers). Oufan Zhang collaborates with scholars based in United States, Canada and Saudi Arabia. Oufan Zhang's co-authors include Omar M. Yaghi, Zhiling Zheng, Christian Borgs, Jennifer Chayes, Teresa Head‐Gordon, Jie Li, Mojtaba Haghighatlari, Julie D. Forman‐Kay, Christopher J. Stein and Itai Leven and has published in prestigious journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and Bioinformatics.

In The Last Decade

Oufan Zhang

16 papers receiving 581 citations

Hit Papers

align trajectories

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.

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

2023 290 citations Zhiling Zheng, Oufan Zhang et al. Journal of the American Chemical Society profile →
ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs

2023 88 citations Zhiling Zheng, Oufan Zhang et al. ACS Central Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Oufan Zhang United States	7	388	158	120	104	66	16	591
Daniil A. Boiko Russia	9	274 0.7×	107 0.7×	53 0.4×	88 0.8×	78 1.2×	27	656
William T. Darrow United States	5	361 0.9×	109 0.7×	119 1.0×	204 2.0×	33 0.5×	6	578
Shih‐Cheng Li Taiwan	11	293 0.8×	116 0.7×	78 0.7×	215 2.1×	24 0.4×	20	547
S. Hessam M. Mehr United Kingdom	12	289 0.7×	105 0.7×	57 0.5×	86 0.8×	39 0.6×	22	566
Riley J. Hickman Canada	14	386 1.0×	102 0.6×	55 0.5×	172 1.7×	49 0.7×	21	827
Lars P. E. Yunker Canada	13	318 0.8×	141 0.9×	82 0.7×	109 1.0×	35 0.5×	17	796
Dylan M. Anstine United States	14	387 1.0×	90 0.6×	132 1.1×	159 1.5×	19 0.3×	24	618
Henrik Schopmans Germany	4	300 0.8×	68 0.4×	30 0.3×	144 1.4×	61 0.9×	7	455
Thomas J. Struble United States	10	350 0.9×	137 0.9×	158 1.3×	269 2.6×	42 0.6×	12	707
Samuel M. Blau United States	16	338 0.9×	110 0.7×	110 0.9×	132 1.3×	27 0.4×	30	897

Countries citing papers authored by Oufan Zhang

Since Specialization

Citations

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

Fields of papers citing papers by Oufan Zhang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Oufan Zhang. 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 Oufan Zhang. The network helps show where Oufan Zhang may publish in the future.

Co-authorship network of co-authors of Oufan Zhang

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

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Zhang, Oufan, et al.. (2025). Biological insights from integrative modeling of intrinsically disordered protein systems. Current Opinion in Structural Biology. 93. 103063–103063. 1 indexed citations

Wang, Yingze, et al.. (2025). SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models. ACS Central Science. 11(11). 2108–2120. 1 indexed citations

Wang, Yingze, Jie Li, Xingyi Guan, et al.. (2025). A workflow to create a high-quality protein–ligand binding dataset for training, validation, and prediction tasks. Digital Discovery. 4(5). 1209–1220. 4 indexed citations

Li, Jie, Oufan Zhang, Yingze Wang, et al.. (2024). Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design. Journal of Chemical Information and Modeling. 64(24). 9082–9097. 2 indexed citations

Zhang, Oufan, et al.. (2024). A curated rotamer library for common post-translational modifications of proteins. Bioinformatics. 40(7). 6 indexed citations

Zhang, Oufan, et al.. (2023). SPyCi-PDB: A modular command-line interface forback-calculating experimental datatypes of protein structures.. The Journal of Open Source Software. 8(85). 4861–4861. 3 indexed citations

Li, Jie, et al.. (2023). Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins. Journal of Chemical Theory and Computation. 19(14). 4689–4700. 5 indexed citations

Zheng, Zhiling, Oufan Zhang, Christian Borgs, Jennifer Chayes, & Omar M. Yaghi. (2023). ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis. Journal of the American Chemical Society. 145(32). 18048–18062. 290 indexed citations breakdown →

Zhang, Oufan, Mojtaba Haghighatlari, Jie Li, et al.. (2023). Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data. The Journal of Chemical Physics. 158(17). 26 indexed citations

10.

Teixeira, João M. C., Oufan Zhang, Jie Li, et al.. (2023). Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments. Bioinformatics. 39(12). 8 indexed citations

11.

Teixeira, João M. C., Jie Li, Robert M. Vernon, et al.. (2023). Idpconformergenerator: A flexible software suite for sampling the conformational space of disordered protein states. Biophysical Journal. 122(3). 204a–204a. 1 indexed citations

12.

Zheng, Zhiling, Oufan Zhang, Ha L. Nguyen, et al.. (2023). ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs. ACS Central Science. 9(11). 2161–2170. 88 indexed citations breakdown →

13.

Teixeira, João M. C., Jie Li, Robert M. Vernon, et al.. (2022). IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States. The Journal of Physical Chemistry A. 126(35). 5985–6003. 39 indexed citations

14.

Naullage, Pavithra M., Mojtaba Haghighatlari, João M. C. Teixeira, et al.. (2022). Protein Dynamics to Define and Refine Disordered Protein Ensembles. The Journal of Physical Chemistry B. 126(9). 1885–1894. 6 indexed citations

15.

Guan, Xingyi, Akshaya Kumar Das, Christopher J. Stein, et al.. (2022). A benchmark dataset for Hydrogen Combustion. Scientific Data. 9(1). 215–215. 18 indexed citations

16.

Haghighatlari, Mojtaba, Jie Li, Xingyi Guan, et al.. (2022). NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery. 1(3). 333–343. 93 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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