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Tingjun Hou

D-Index & Metrics

Chemistry

D-Index
90
Citations
29791
World Ranking
2062
National Ranking
366

Tingjun Hou publication distribution in Chemistry in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Chemistry in 2026. The highlighted bar marks where Tingjun Hou sits on this spectrum.

61–80 publications: 66 scientists 81–100 publications: 302 scientists 101–120 publications: 623 scientists 121–140 publications: 918 scientists 141–160 publications: 1,218 scientists 161–180 publications: 1,350 scientists 181–200 publications: 1,344 scientists 201–220 publications: 1,281 scientists 221–240 publications: 1,216 scientists 241–260 publications: 1,100 scientists 261–280 publications: 979 scientists 281–300 publications: 939 scientists 301–320 publications: 764 scientists 321–340 publications: 643 scientists 341–360 publications: 628 scientists 361–380 publications: 522 scientists 381–400 publications: 459 scientists 401–420 publications: 397 scientists 421–440 publications: 327 scientists 441–460 publications: 270 scientists 461–480 publications: 265 scientists 481–500 publications: 252 scientists 501–520 publications: 201 scientists 521–540 publications: 185 scientists 541–560 publications: 148 scientists 561–580 publications: 148 scientists 581–600 publications: 132 scientists 601–620 publications: 114 scientists 621–640 publications: 104 scientists 641–660 publications: 91 scientists 661–680 publications: 92 scientists 681–700 publications: 73 scientists 701–720 publications: 57 scientists 721–740 publications: 54 scientists 741–760 publications: 67 scientists 761–780 publications: 45 scientists 781–800 publications: 46 scientists 801–820 publications: 39 scientists 821–840 publications: 32 scientists 841–860 publications: 36 scientists 861–880 publications: 29 scientists 881–900 publications: 26 scientists 901–920 publications: 24 scientists 921–940 publications: 14 scientists 941–960 publications: 23 scientists 961–980 publications: 28 scientists 981–1,000 publications: 15 scientists 1,001–1,020 publications: 29 scientists 1,021–1,040 publications: 12 scientists 1,041–1,060 publications: 19 scientists 1,061–1,080 publications: 12 scientists 1,081–1,100 publications: 6 scientists 1,101–1,120 publications: 8 scientists 1,121–1,140 publications: 12 scientists 1,141–1,160 publications: 5 scientists 1,161–1,180 publications: 6 scientists 1,181–1,200 publications: 14 scientists 1,201–1,220 publications: 7 scientists 1,221–1,240 publications: 2 scientists 1,241–1,260 publications: 6 scientists 1,261–1,280 publications: 4 scientists 1,281–1,294 publications: 6 scientists 1,295+ publications: 100 scientists
61 publications 1,295+

This scientist: 332 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,295 publications or more.

Tingjun Hou D-index placement in Chemistry in 2026

The chart shows the D-index (discipline H-index) distribution of Chemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Tingjun Hou sits on this spectrum.

40–41 D-Index: 289 scientists 42–43 D-Index: 612 scientists 44–45 D-Index: 808 scientists 46–47 D-Index: 776 scientists 48–49 D-Index: 835 scientists 50–51 D-Index: 861 scientists 52–53 D-Index: 872 scientists 54–55 D-Index: 933 scientists 56–57 D-Index: 1,051 scientists 58–59 D-Index: 930 scientists 60–61 D-Index: 882 scientists 62–63 D-Index: 834 scientists 64–65 D-Index: 731 scientists 66–67 D-Index: 775 scientists 68–69 D-Index: 683 scientists 70–71 D-Index: 646 scientists 72–73 D-Index: 561 scientists 74–75 D-Index: 501 scientists 76–77 D-Index: 437 scientists 78–79 D-Index: 388 scientists 80–81 D-Index: 354 scientists 82–83 D-Index: 292 scientists 84–85 D-Index: 275 scientists 86–87 D-Index: 254 scientists 88–89 D-Index: 235 scientists 90–91 D-Index: 185 scientists 92–93 D-Index: 192 scientists 94–95 D-Index: 155 scientists 96–97 D-Index: 163 scientists 98–99 D-Index: 125 scientists 100–101 D-Index: 105 scientists 102–103 D-Index: 105 scientists 104–105 D-Index: 112 scientists 106–107 D-Index: 88 scientists 108–109 D-Index: 68 scientists 110–111 D-Index: 69 scientists 112–113 D-Index: 65 scientists 114–115 D-Index: 79 scientists 116–117 D-Index: 61 scientists 118–119 D-Index: 44 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 40 scientists 124–125 D-Index: 33 scientists 126–127 D-Index: 26 scientists 128–129 D-Index: 34 scientists 130–131 D-Index: 35 scientists 132–133 D-Index: 25 scientists 134–135 D-Index: 27 scientists 136–137 D-Index: 17 scientists 138–139 D-Index: 16 scientists 140–141 D-Index: 20 scientists 142–143 D-Index: 20 scientists 144–145 D-Index: 15 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 9 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 11 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 3 scientists 158 D-Index: 3 scientists 159+ D-Index: 98 scientists
40 D-Index 159+

This scientist: 90 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 159 D-Index or more.

Overview

Tingjun Hou is affiliated with Zhejiang University in China and has contributed extensively to the fields of biochemistry, genetics, molecular biology, and computer science. Their research spans multiple subfields, including molecular biology, computational theory and mathematics, materials chemistry, pharmacology, and organic chemistry.

The scientist's work focuses on topics such as computational drug discovery methods, machine learning applications in materials science and bioinformatics, protein structure and dynamics, chemical synthesis and analysis, protein degradation and inhibitors, as well as studies related to estrogen and related hormone effects.

Frequent publication venues for Tingjun Hou include the Journal of Chemical Information and Modeling, Briefings in Bioinformatics, the Journal of Medicinal Chemistry, arXiv (Cornell University), and Nature Communications. These journals reflect an interdisciplinary approach bridging chemistry, biology, and computational sciences.

Key recent publications are as follows:

  • ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties, 2021, Nucleic Acids Research
  • ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support, 2024, Nucleic Acids Research
  • Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models, 2021, Journal of Cheminformatics
  • A unified drug-target interaction prediction framework based on knowledge graph and recommendation system, 2021, Nature Communications
  • InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions, 2021, Journal of Medicinal Chemistry

Tingjun Hou collaborates frequently with several researchers, including Chang-Yu Hsieh, Yu Kang, Dongsheng Cao, Chao Shen, and Xujun Zhang. These collaborations indicate active engagement with peers in related scientific domains.

Best Publications

  • Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations.

    Tingjun Hou;Junmei Wang;Youyong Li;Wei Wang

  • End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

    Ercheng Wang;Huiyong Sun;Junmei Wang;Zhe Wang

  • 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

    Tingjun Hou;Junmei Wang;Youyong Li;Wei Wang

  • Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.

    Zhe Wang;Huiyong Sun;Xiaojun Yao;Dan Li

  • 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

    Huiyong Sun;Huiyong Sun;Youyong Li;Sheng Tian;Lei Xu

  • Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

    Dejun Jiang;Zhenxing Wu;Chang-Yu Hsieh;Guangyong Chen

  • 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.

    Fu Chen;Hui Liu;Huiyong Sun;Peichen Pan

  • 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

    Huiyong Sun;Huiyong Sun;Youyong Li;Mingyun Shen;Sheng Tian

  • The application of in silico drug-likeness predictions in pharmaceutical research

    Sheng Tian;Junmei Wang;Youyong Li;Dan Li

  • Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models.

    Lei Xu;Huiyong Sun;Youyong Li;Junmei Wang

  • Recent advances in free energy calculations with a combination of molecular mechanics and continuum models

    Junmei M. Wang;Tingjun Hou;Tingjun Hou;Xiaojie Xu

  • ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

    Tingjun Hou;Xiaojie Xu

  • Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches

    Huiyong Sun;Lili Duan;Fu Chen;Hui Liu

  • Janus Structures of Transition Metal Dichalcogenides as the Heterojunction Photocatalysts for Water Splitting

    Yujin Ji;Mingye Yang;Haiping Lin;Tingjun Hou

  • ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach

    Tingjun Hou;Ke Xia;Wei Zhang;Xiaojie Xu

  • Molecular dynamics and free energy studies on the wild-type and double mutant HIV-1 protease complexed with amprenavir and two amprenavir-related inhibitors: mechanism for binding and drug resistance.

    Unknown

  • ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification.

    Tingjun Hou;Junmei Wang;Wei Zhang;Xiaojie Xu

  • Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

    Tingjun Hou;Xiaojie Xu

  • ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

    Tingjun Hou;Wei Zhang;Ke Xia;Xuebin Qiao

  • Characterization of Domain–Peptide Interaction Interface: Prediction of SH3 Domain-Mediated Protein–Protein Interaction Network in Yeast by Generic Structure-Based Models

    Tingjun Hou;Nan Li;Youyong Li;Wei Wang

  • Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain.

    Tingjun Hou;Wei Zhang;David A. Case;Wei Wang

  • ADME Evaluation in Drug Discovery. 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules?

    Tingjun Hou;Junmei Wang;Wei Zhang;Xiaojie Xu

  • Recent advances in computational prediction of drug absorption and permeability in drug discovery.

    Tingjun Hou;Junmei Wang;Wei Zhang;Wei Wang

Frequent Co-Authors

Youyong Li
Youyong Li Soochow University
Junmei Wang
Junmei Wang University of Pittsburgh
Dong-Sheng Cao
Dong-Sheng Cao Central South University
Huilong Dong
Huilong Dong Soochow University
Shuit-Tong Lee
Shuit-Tong Lee Macau University of Science and Technology
Wei Wang
Wei Wang University of California, San Diego
Feng Zhu
Feng Zhu Zhejiang University
Aiping Lu
Aiping Lu Hong Kong Baptist University
Haiping Lin
Haiping Lin Soochow University, Taiwan
Simon Ming-Yuen Lee
Simon Ming-Yuen Lee University of Macau

External Links

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Best Scientists Citing Tingjun Hou

Xiaojun Yao

Xiaojun Yao

Macau University of Science and Technology

Publications: 86

John Z. H. Zhang

John Z. H. Zhang

New York University

Publications: 68

Huanxiang Liu

Huanxiang Liu

Lanzhou University

Publications: 60

Hong-Xing Zhang

Hong-Xing Zhang

Jilin University

Publications: 36

Chandra S. Verma

Chandra S. Verma

Agency for Science, Technology and Research

Publications: 29

Holger Gohlke

Holger Gohlke

Heinrich Heine University Düsseldorf

Publications: 29

Dong-Sheng Cao

Dong-Sheng Cao

Central South University

Publications: 28

Feng Zhu

Feng Zhu

Zhejiang University

Publications: 24

Junmei Wang

Junmei Wang

University of Pittsburgh

Publications: 24

Hualiang Jiang

Hualiang Jiang

Chinese Academy of Sciences

Publications: 23

Dong-Qing Wei

Dong-Qing Wei

Shanghai Jiao Tong University

Publications: 23

Youyong Li

Youyong Li

Soochow University

Publications: 22

Benedetta Mennucci

Benedetta Mennucci

University of Pisa

Publications: 21

Rajeev Ahuja

Rajeev Ahuja

Uppsala University

Publications: 20

Ruth Nussinov

Ruth Nussinov

National Institutes of Health

Publications: 19

Aamer Saeed

Aamer Saeed

Quaid-i-Azam University

Publications: 18

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