Tingjun Hou

39.1k total citations · 17 hit papers
554 papers, 29.3k citations indexed

About

Tingjun Hou is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Tingjun Hou has authored 554 papers receiving a total of 29.3k indexed citations (citations by other indexed papers that have themselves been cited), including 339 papers in Molecular Biology, 262 papers in Computational Theory and Mathematics and 142 papers in Materials Chemistry. Recurrent topics in Tingjun Hou's work include Computational Drug Discovery Methods (262 papers), Protein Structure and Dynamics (125 papers) and Machine Learning in Materials Science (83 papers). Tingjun Hou is often cited by papers focused on Computational Drug Discovery Methods (262 papers), Protein Structure and Dynamics (125 papers) and Machine Learning in Materials Science (83 papers). Tingjun Hou collaborates with scholars based in China, United States and Macao. Tingjun Hou's co-authors include Youyong Li, Junmei Wang, Huiyong Sun, Zhe Wang, Lei Xu, Dongsheng Cao, Wei Wang, Sheng Tian, Dan Li and Ercheng Wang and has published in prestigious journals such as Nature, Chemical Reviews and Proceedings of the National Academy of Sciences.

In The Last Decade

Tingjun Hou

532 papers receiving 28.9k citations

Hit Papers

Assessing the Performance of the MM/PBSA and MM/GBSA Meth... 2010 2026 2015 2020 2010 2021 2019 2016 2010 500 1000 1.5k 2.0k
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.

  1. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations
    2010 2.1k citations Tingjun Hou, Junmei Wang et al. Journal of Chemical Information and Modeling profile →
  2. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
    2021 1.7k citations Zhenhua Wu, Jiacai Yi et al. profile →
  3. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design
    2019 1.5k citations Ercheng Wang, Huiyong Sun et al. profile →
  4. Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power
    2016 700 citations Zhe Wang, Huiyong Sun et al. profile →
  5. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking
    2010 636 citations Tingjun Hou, Junmei Wang et al. profile →
  6. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set
    2014 620 citations Huiyong Sun, Youyong Li et al. profile →
  7. Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring
    2014 460 citations Huiyong Sun, Youyong Li et al. profile →
  8. HawkDock: a web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA
    2019 420 citations Ercheng Wang, Zhe Wang et al. profile →
  9. Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models
    2013 417 citations Lei Xu, Huiyong Sun et al. profile →
  10. 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 →
  11. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking
    2016 374 citations Huiyong Sun, Peichen Pan et al. profile →
  12. The application of in silico drug-likeness predictions in pharmaceutical research
    2015 360 citations Junmei Wang, Youyong Li et al. profile →
  13. ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support
    2024 349 citations Jiacai Yi, Zhenhua Wu et al. profile →
  14. PROTAC-DB 3.0: an updated database of PROTACs with extended pharmacokinetic parameters
    2024 44 citations Jingxuan Ge, Huating Wang et al. profile →
  15. 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 →
  16. 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 →
  17. Artificial intelligence in peptide-based drug design
    2025 19 citations Silong Zhai, Dan Li et al. Drug Discovery Today profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tingjun Hou China 81 16.1k 10.6k 5.4k 3.8k 2.6k 554 29.3k
Junmei Wang China 67 25.1k 1.6× 7.7k 0.7× 7.1k 1.3× 6.0k 1.6× 3.2k 1.2× 416 45.8k
David E. Shaw United States 85 29.5k 1.8× 7.8k 0.7× 6.1k 1.1× 4.1k 1.1× 4.2k 1.6× 282 43.5k
G. Klebe Germany 65 14.6k 0.9× 8.8k 0.8× 3.8k 0.7× 5.0k 1.3× 1.3k 0.5× 384 22.2k
Alexander Tropsha United States 67 9.5k 0.6× 13.7k 1.3× 4.5k 0.8× 4.7k 1.2× 1000 0.4× 307 23.8k
Thomas E. Cheatham United States 61 29.0k 1.8× 4.0k 0.4× 4.9k 0.9× 3.3k 0.9× 2.4k 0.9× 185 39.1k
Holger Gohlke Germany 49 16.4k 1.0× 4.9k 0.5× 3.4k 0.6× 2.5k 0.7× 1.8k 0.7× 283 23.1k
Christopher I. Bayly United States 33 17.5k 1.1× 4.0k 0.4× 5.8k 1.1× 4.3k 1.1× 1.7k 0.6× 77 29.0k
Brian K. Shoichet United States 97 24.6k 1.5× 15.6k 1.5× 4.7k 0.9× 5.2k 1.4× 2.2k 0.8× 260 36.4k
Kenneth M. Merz United States 73 23.0k 1.4× 5.0k 0.5× 8.7k 1.6× 6.7k 1.8× 2.7k 1.0× 374 41.5k
Piotr Cieplak United States 46 26.4k 1.6× 3.9k 0.4× 7.3k 1.4× 4.8k 1.3× 2.6k 1.0× 126 41.0k

Countries citing papers authored by Tingjun Hou

Since Specialization
Citations

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

Fields of papers citing papers by Tingjun Hou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tingjun Hou

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

All Works

20 of 20 papers shown
1.
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
2.
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
3.
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 →
4.
Wang, Ying, Xinyue Wang, Xin Chai, et al.. (2025). Discovery of N-(1,2,4-Thiadiazol-5-yl)benzo[b]oxepine-4-carboxamide Derivatives as Novel Antiresistance Androgen Receptor Antagonists. Journal of Medicinal Chemistry. 68(3). 3445–3459.
5.
Chen, Kepeng, Jike Wang, Dan Li, et al.. (2025). Effective generation of heavy-atom-free triplet photosensitizers containing multiple intersystem crossing mechanisms based on deep learning. Chemical Science. 16(32). 14698–14709. 1 indexed citations
6.
Zhai, Silong, Jike Wang, Stephanie Lin, et al.. (2025). PepPCBench is a Comprehensive Benchmarking Framework for Protein–Peptide Complex Structure Prediction. Journal of Chemical Information and Modeling. 65(16). 8497–8513. 4 indexed citations
7.
Li, Dan, et al.. (2025). Discovery of novel tetrahydroquinoline derivatives as potent, selective, and orally Available AR antagonists. European Journal of Medicinal Chemistry. 291. 117566–117566.
8.
Jiang, Dejun, Zhe Wang, Huiyong Sun, et al.. (2024). TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides. Journal of Chemical Information and Modeling. 64(13). 5016–5027. 2 indexed citations
9.
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
10.
Shi, Rui, Peichen Pan, Rui Lv, et al.. (2022). High-throughput glycolytic inhibitor discovery targeting glioblastoma by graphite dots–assisted LDI mass spectrometry. Science Advances. 8(7). eabl4923–eabl4923. 26 indexed citations
11.
Yi, Jiacai, et al.. (2022). ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images. Briefings in Bioinformatics. 23(2). 15 indexed citations
12.
Mao, Chunyou, Chanjuan Xu, Nan Jin, et al.. (2021). Structural basis of GABAB receptor–Gi protein coupling. Nature. 594(7864). 594–598. 69 indexed citations
13.
Hu, Xueping, Xin Chai, Qing Ye, et al.. (2021). Opportunities for overcoming tuberculosis: Emerging targets and their inhibitors. Drug Discovery Today. 27(1). 326–336. 16 indexed citations
14.
Xu, Xiaoyan, Yaqin Sun, Xufeng Cen, et al.. (2021). Metformin activates chaperone-mediated autophagy and improves disease pathologies in an Alzheimer disease mouse model. Protein & Cell. 12(10). 769–787. 121 indexed citations
15.
Liu, Na, Wenfang Zhou, Yue Guo, et al.. (2018). Molecular Dynamics Simulations Revealed the Regulation of Ligands to the Interactions between Androgen Receptor and Its Coactivator. Journal of Chemical Information and Modeling. 58(8). 1652–1661. 40 indexed citations
16.
Feng, Zhiwei, Tingjun Hou, & Youyong Li. (2012). Unidirectional peristaltic movement in multisite drug binding pockets of AcrB from molecular dynamics simulations. Molecular BioSystems. 8(10). 2699–2709. 26 indexed citations
17.
Wei, Yinxiang, Yuanfang Ma, Qing Zhao, et al.. (2012). New Use for an Old Drug: Inhibiting ABCG2 with Sorafenib. Molecular Cancer Therapeutics. 11(8). 1693–1702. 40 indexed citations
18.
McLaughlin, William A., Tingjun Hou, Susan S. Taylor, & Weili Wang. (2010). The identification of novel cyclic AMP-dependent protein kinase anchoring proteins using bioinformatic filters and peptide arrays. Protein Engineering Design and Selection. 24(3). 333–339. 5 indexed citations
19.
McLaughlin, William A., Tingjun Hou, & Wei Wang. (2006). Prediction of Binding Sites of Peptide Recognition Domains: An Application on Grb2 and SAP SH2 Domains. Journal of Molecular Biology. 357(4). 1322–1334. 13 indexed citations
20.
Hou, Tingjun & Xiaojie Xu. (2002). ADME evaluation in drug discovery. Journal of Molecular Modeling. 8(12). 337–349. 222 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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Breakdown of academic impact, for papers by Junmei Wang Breakdown of academic impact, for papers by David E. Shaw Breakdown of academic impact, for papers by G. Klebe Breakdown of academic impact, for papers by Alexander Tropsha Breakdown of academic impact, for papers by Thomas E. Cheatham Breakdown of academic impact, for papers by Holger Gohlke Breakdown of academic impact, for papers by Christopher I. Bayly Breakdown of academic impact, for papers by Brian K. Shoichet Breakdown of academic impact, for papers by Kenneth M. Merz Breakdown of academic impact, for papers by Piotr Cieplak
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