Mingxun Wang

43.6k total citations · 4 hit papers
66 papers, 3.7k citations indexed

About

Mingxun Wang is a scholar working on Molecular Biology, Spectroscopy and Computational Theory and Mathematics. According to data from OpenAlex, Mingxun Wang has authored 66 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 22 papers in Spectroscopy and 13 papers in Computational Theory and Mathematics. Recurrent topics in Mingxun Wang's work include Metabolomics and Mass Spectrometry Studies (40 papers), Mass Spectrometry Techniques and Applications (13 papers) and Computational Drug Discovery Methods (13 papers). Mingxun Wang is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (40 papers), Mass Spectrometry Techniques and Applications (13 papers) and Computational Drug Discovery Methods (13 papers). Mingxun Wang collaborates with scholars based in United States, Germany and Netherlands. Mingxun Wang's co-authors include Pieter C. Dorrestein, Louis‐Félix Nothias, Nuno Bandeira, Justin J. J. van der Hooft, Andrés Mauricio Caraballo‐Rodríguez, Jeremy Carver, Ricardo Silva, Kyo Bin Kang, Theodore Alexandrov and William H. Gerwick and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Mingxun Wang

58 papers receiving 3.7k 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.

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

2016 654 citations Mingxun Wang, Nuno Bandeira et al. profile →
Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation

2018 284 citations Louis‐Félix Nothias, Mélissa Nothias-Esposito et al. Journal of Natural Products profile →
MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

2019 277 citations Kyo Bin Kang, Andrés Mauricio Caraballo‐Rodríguez et al. profile →
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

2021 264 citations Hyun Woo Kim, Mingxun Wang et al. Journal of Natural Products profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mingxun Wang United States	25	2.6k	665	642	331	304	66	3.7k
Louis‐Félix Nothias United States	29	2.8k 1.1×	942 1.4×	536 0.8×	594 1.8×	433 1.4×	59	4.1k
Kai Dührkop Germany	18	2.5k 1.0×	379 0.6×	943 1.5×	410 1.2×	454 1.5×	29	3.6k
Tomáš Pluskal United States	25	3.4k 1.3×	618 0.9×	836 1.3×	794 2.4×	334 1.1×	45	5.0k
Jeramie D. Watrous United States	28	2.3k 0.9×	616 0.9×	744 1.2×	434 1.3×	92 0.3×	66	3.8k
Daniel Petras United States	31	1.7k 0.7×	500 0.8×	335 0.5×	384 1.2×	134 0.4×	92	3.1k
Alexey V. Melnik United States	22	1.5k 0.6×	387 0.6×	313 0.5×	255 0.8×	126 0.4×	36	2.5k
Alejandro Villar‐Briones Japan	17	2.5k 1.0×	349 0.5×	650 1.0×	495 1.5×	83 0.3×	23	3.7k
Sandra Castillo Finland	15	2.7k 1.0×	343 0.5×	843 1.3×	456 1.4×	95 0.3×	27	3.9k
Ricardo Silva Brazil	24	1.3k 0.5×	336 0.5×	280 0.4×	278 0.8×	136 0.4×	67	2.2k
Markus Fleischauer Germany	7	1.3k 0.5×	243 0.4×	405 0.6×	288 0.9×	194 0.6×	9	2.0k

Countries citing papers authored by Mingxun Wang

Since Specialization

Citations

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

Fields of papers citing papers by Mingxun Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingxun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Mingxun Wang. A scholar is included among the top collaborators of Mingxun Wang 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 Mingxun Wang. Mingxun Wang 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:

20 of 20 papers shown

Xing, Shipei, et al.. (2025). Reverse Spectral Search Reimagined: A Simple but Overlooked Solution for Chimeric Spectral Annotation. Analytical Chemistry. 97(33). 17926–17930. 1 indexed citations

Schubert, Julian, Giovanni Andrea Vitale, Jürgen Fleischer, et al.. (2025). High-Frequency Microfluidic Fractionation for Compound-Resolved Bioactivity-Based Metabolomics. Analytical Chemistry. 97(43). 24093–24104.

Wang, Mingxun, et al.. (2025). A versatile toolkit for drug metabolism studies with GNPS2: from drug development to clinical monitoring. Nature Protocols.

Mannochio-Russo, Helena, Santosh Lamichhane, Shipei Xing, et al.. (2025). A guide to reverse metabolomics—a framework for big data discovery strategy. Nature Protocols. 20(10). 2960–2993. 6 indexed citations

Canchola, Alexa, Kunpeng Chen, Chi‐Yun Chen, et al.. (2025). Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media. ACS Nano. 19(43). 37633–37650. 4 indexed citations

Abiead, Yasin El, Deepa Acharya, Christopher J. Brown, et al.. (2025). MS-RT: A Method for Evaluating MS/MS Clustering Performance for Metabolomics Data. Journal of Proteome Research. 24(4). 1778–1790. 2 indexed citations

Hecht, Helge, et al.. (2024). Reproducible MS/MS library cleaning pipeline in matchms. Journal of Cheminformatics. 16(1). 88–88. 8 indexed citations

Vitale, Giovanni Andrea, et al.. (2024). Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products. Natural Product Reports. 41(6). 885–904. 20 indexed citations

Yasaka, Tyler M., Liang Lü, Aditya Bhagwat, et al.. (2024). Fast mass spectrometry search and clustering of untargeted metabolomics data. Nature Biotechnology. 42(11). 1672–1677. 14 indexed citations

10.

Moyo, Phanankosi, et al.. (2023). Leveraging off higher plant phylogenetic insights for antiplasmodial drug discovery. Natural Products and Bioprospecting. 13(1). 35–35.

11.

Moyo, Phanankosi, et al.. (2023). Prioritised identification of structural classes of natural products from higher plants in the expedition of antimalarial drug discovery. Natural Products and Bioprospecting. 13(1). 37–37. 1 indexed citations

12.

Wang, Mingxun, Zhiyuan Bo, Haitao Yu, et al.. (2023). Synergistic Effect of Lenvatinib and Chemotherapy in Hepatocellular Carcinoma Using Preclinical Models. Journal of Hepatocellular Carcinoma. Volume 10. 483–495. 3 indexed citations

13.

Freitas, Michael A., et al.. (2022). TIMSCONVERT: a workflow to convert trapped ion mobility data to open data formats. Bioinformatics. 38(16). 4046–4047. 10 indexed citations

14.

Leão, Tiago, Mingxun Wang, Ricardo Silva, et al.. (2022). NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters. PNAS Nexus. 1(5). pgac257–pgac257. 16 indexed citations

15.

Nothias, Louis‐Félix, Mingxun Wang, Dong‐Hyun Kim, et al.. (2022). Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies. Analytical Chemistry. 94(2). 1456–1464. 33 indexed citations

16.

Bittremieux, Wout, Mingxun Wang, & Pieter C. Dorrestein. (2022). The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics. 18(12). 94–94. 61 indexed citations

17.

Abiead, Yasin El, Christoph Bueschl, Mingxun Wang, et al.. (2022). Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets. Analytica Chimica Acta. 1229. 340352–340352. 10 indexed citations

18.

Kim, Hyun Woo, Mingxun Wang, Christopher A. Leber, et al.. (2021). NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. Journal of Natural Products. 84(11). 2795–2807. 264 indexed citations breakdown →

19.

Tian, Xiaodong, Mingxun Wang, Liu W, et al.. (2018). A primary retroperitoneal anaplastic lymphoma kinase-positive anaplastic large cell lymphoma with tumor thrombosis. SHILAP Revista de lepidopterología.

20.

Nothias, Louis‐Félix, Mélissa Nothias-Esposito, Ricardo Silva, et al.. (2018). Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation. Journal of Natural Products. 81(4). 758–767. 284 indexed citations breakdown →

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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