Xiaotian Kong

598 total citations
17 papers, 517 citations indexed

About

Xiaotian Kong is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Xiaotian Kong has authored 17 papers receiving a total of 517 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Xiaotian Kong's work include Computational Drug Discovery Methods (5 papers), Lung Cancer Treatments and Mutations (4 papers) and Protein Structure and Dynamics (3 papers). Xiaotian Kong is often cited by papers focused on Computational Drug Discovery Methods (5 papers), Lung Cancer Treatments and Mutations (4 papers) and Protein Structure and Dynamics (3 papers). Xiaotian Kong collaborates with scholars based in China, Czechia and United States. Xiaotian Kong's co-authors include Tingjun Hou, Youyong Li, Peichen Pan, Huiyong Sun, Sheng Tian, Dan Li, Wenfang Zhou, Xuwen Wang, Hongguang Xia and Jingpeng Wang and has published in prestigious journals such as Environmental Science & Technology, Scientific Reports and Journal of Medicinal Chemistry.

In The Last Decade

Xiaotian Kong

15 papers receiving 514 citations

Peers

Xiaotian Kong

CTM

ONCOL

PRM

Jang‐Sik Choi South Korea

Luisa Shin Ogawa United States

Sujie Zhu China

Akito Hata Japan

Ulrike Pfaar Switzerland

Chon‐Kit Chou Taiwan

Marina Sekacheva Russia

Jane Kepros United States

Stéphanie Blockhuys Sweden

Vojo Vukovic United States

Jang‐Sik Choi South Korea

Xiaotian Kong

149 ×0.5

70 ×0.6

CTM

85 ×0.8

ONCOL

10 ×0.1

PRM

44 ×0.7

Citations per year, relative to Xiaotian Kong Xiaotian Kong (= 1×) peers Jang‐Sik Choi

Countries citing papers authored by Xiaotian Kong

Since Specialization

Citations

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

Fields of papers citing papers by Xiaotian Kong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaotian Kong

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

All Works

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17 of 17 papers shown

Tian, Sheng, Kai Wang, Xiaotian Kong, et al.. (2025). Computational discovery and repurposing of chloramphenicol succinate as a potent P2Y14 receptor antagonist for inflammatory bowel disease therapy. Journal of Advanced Research.

Kong, Xiaotian, Yushi Zou, Peng Cao, et al.. (2025). Allosteric Cooperativity Mechanism Investigation of Orthosteric and Allosteric Ligands in Modulating AR Activity: A Molecular Dynamics Study. Journal of Chemical Information and Modeling. 65(20). 11297–11313.

Wu, Zhixiang, et al.. (2024). ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein–DNA Interaction Hotspots. Journal of Chemical Information and Modeling. 64(8). 3548–3557. 2 indexed citations

Zhao, Yulong, Xiaotian Kong, Yaxuan Wang, et al.. (2024). Prediction of Drug‐Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches. Chemical Biology & Drug Design. 104(2). e14607–e14607. 4 indexed citations

Li, Qingyu, Xiaotian Kong, Jiale Xu, et al.. (2023). MnFe2O4/ rGO/Diatomite composites with excellent wideband electromagnetic microwave absorption. Journal of Alloys and Compounds. 941. 168851–168851. 29 indexed citations

Kong, Xiaotian, Keren Zhang, Nana Zhang, et al.. (2023). FOXA1 O-GlcNAcylation–mediated transcriptional switch governs metastasis capacity in breast cancer. Science Advances. 9(33). eadg7112–eadg7112. 27 indexed citations

Kong, Xiaotian, Enming Xing, Sijin Wu, et al.. (2022). Computational modeling studies reveal the origin of the binding preference of 3‐(3,4‐di hydroisoquinolin‐2(1H)‐ylsulfonyl)benzoic acids for AKR1C3 over its isoforms. Protein Science. 31(12). e4499–e4499. 4 indexed citations

Kong, Xiaotian, Peichen Pan, Huiyong Sun, et al.. (2019). Drug Discovery Targeting Anaplastic Lymphoma Kinase (ALK). Journal of Medicinal Chemistry. 62(24). 10927–10954. 107 indexed citations

Kong, Xiaotian, Huiyong Sun, Peichen Pan, et al.. (2018). Importance of protein flexibility in molecular recognition: a case study on Type-I1/2 inhibitors of ALK. Physical Chemistry Chemical Physics. 20(7). 4851–4863. 24 indexed citations

10.

Pan, Peichen, Huidong Yu, Xiaotian Kong, et al.. (2017). Combating Drug-Resistant Mutants of Anaplastic Lymphoma Kinase with Potent and Selective Type-I^1/2 Inhibitors by Stabilizing Unique DFG-Shifted Loop Conformation. ACS Central Science. 3(11). 1208–1220. 45 indexed citations

11.

Kong, Xiaotian, Huiyong Sun, Peichen Pan, et al.. (2017). How Does the L884P Mutation Confer Resistance to Type-II Inhibitors of JAK2 Kinase: A Comprehensive Molecular Modeling Study. Scientific Reports. 7(1). 9088–9088. 18 indexed citations

12.

Ding, Keke, Xiaotian Kong, Jingpeng Wang, et al.. (2017). Side Chains of Parabens Modulate Antiandrogenic Activity: In Vitro and Molecular Docking Studies. Environmental Science & Technology. 51(11). 6452–6460. 106 indexed citations

13.

Sun, Huiyong, Peichen Pan, Sheng Tian, et al.. (2016). Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery. Scientific Reports. 6(1). 24817–24817. 66 indexed citations

14.

Pan, Peichen, Huiyong Sun, Hui Liu, et al.. (2016). In Silico Exploration for Novel Type-I Inhibitors of Tie-2/TEK: The Performance of Different Selection Strategy in Selecting Virtual Screening Candidates. Scientific Reports. 6(1). 37628–37628. 5 indexed citations

15.

Kong, Xiaotian, Peichen Pan, Dan Li, et al.. (2015). Importance of protein flexibility in ranking inhibitor affinities: modeling the binding mechanisms of piperidine carboxamides as Type I1/2 ALK inhibitors. Physical Chemistry Chemical Physics. 17(8). 6098–6113. 48 indexed citations

16.

Kong, Xiaotian, Huiyong Sun, Peichen Pan, et al.. (2015). Molecular principle of the cyclin-dependent kinase selectivity of 4-(thiazol-5-yl)-2-(phenylamino) pyrimidine-5-carbonitrile derivatives revealed by molecular modeling studies. Physical Chemistry Chemical Physics. 18(3). 2034–2046. 20 indexed citations

17.

Pan, Peichen, Sheng Tian, Huiyong Sun, et al.. (2015). Identification and Preliminary SAR Analysis of Novel Type-I Inhibitors of TIE-2 via Structure-Based Virtual Screening and Biological Evaluation in in vitro Models. Journal of Chemical Information and Modeling. 55(12). 2693–2704. 12 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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