World's Best Scientists 2026 revealed!
Huanxiang Liu

Huanxiang Liu

D-Index & Metrics

Chemistry

D-Index
48
Citations
9434
World Ranking
15146
National Ranking
2349

Huanxiang Liu 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 Huanxiang Liu 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: 273 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Huanxiang Liu 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 Huanxiang Liu 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: 48 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Huanxiang Liu is affiliated with Lanzhou University in China and has contributed extensively to the fields of biochemistry, genetics, and molecular biology with a particular focus on molecular biology and computational approaches.

Their research encompasses several main topics, including:

  • Computational Drug Discovery Methods
  • Protein Structure and Dynamics
  • Machine Learning in Materials Science
  • Cancer Therapeutics and Mechanisms
  • Machine Learning in Bioinformatics
  • RNA and Protein Synthesis Mechanisms
  • Receptor Mechanisms and Signaling

Liu has published in numerous scientific venues, with frequent appearances in:

  • Journal of Chemical Information and Modeling
  • ACS Chemical Neuroscience
  • International Journal of Molecular Sciences
  • Briefings in Bioinformatics
  • Wiley Interdisciplinary Reviews Computational Molecular Science

Selected recent papers include:

  • "MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm," 2020, Briefings in Bioinformatics
  • "CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity," 2020, Environmental Health Perspectives
  • "Ligand recognition and allosteric regulation of DRD1-Gs signaling complexes," 2021, Cell
  • "Application advances of deep learning methods for de novo drug design and molecular dynamics simulation," 2021, Wiley Interdisciplinary Reviews Computational Molecular Science
  • "RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions," 2021, Chemical Engineering Journal

Frequent co-authors in Liu's body of work include:

  • Xiaojun Yao
  • Qianqian Zhang
  • Tingjun Hou
  • Shuoyan Tan
  • Henry H. Y. Tong

Their scholarship bridges computational theory and mathematics with applied molecular sciences, reflecting an interdisciplinary approach. The combination of molecular biology, machine learning, and materials chemistry is evident in their research output.

Overall, the scientific contributions cover fundamental and computational drug discovery methods, detailed studies on protein structure and dynamics, and the integration of machine learning techniques into molecular simulation and retrosynthesis prediction models. This body of work intersects medicine and biochemistry, aiming to advance understanding in molecular mechanisms and therapeutic strategies.

Best Publications

  • Molecular dynamics simulations and novel drug discovery.

    Xuewei Liu;Danfeng Shi;Shuangyan Zhou;Hongli Liu

  • Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

    Iurii Sushko;Sergii Novotarskyi;Robert Körner;Anil Kumar Pandey

  • MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.

    Qifeng Bai;Shuoyan Tan;Tingyang Xu;Huanxiang Liu

  • CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

    Kamel Mansouri;Nicole Kleinstreuer;Ahmed M. Abdelaziz;Domenico Alberga

  • Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine.

    Huanxiang Liu;Xiaojun Yao;Ruisheng Zhang;Mancang Liu

  • Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM–GBSA calculation

    Juan Du;Huijun Sun;Lili Xi;Jiazhong Li

  • Diagnosing breast cancer based on support vector machines.

    Huanxiang Liu;Ruisheng Zhang;Feng Luan;Xiaojun Yao

  • Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs.

    Huanxiang Liu;Ruisheng Zhang;Xiaojun Yao;Mancang Liu

  • The molecular mechanism of bisphenol A (BPA) as an endocrine disruptor by interacting with nuclear receptors: insights from molecular dynamics (MD) simulations.

    Lanlan Li;Qianqian Wang;Yan Zhang;Yuzhen Niu

  • Ligand recognition and allosteric regulation of DRD1-Gs signaling complexes

    Peng Xiao;Wei Yan;Lu Gou;Ya Ni Zhong

  • Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

    Qifeng Bai;Shuo Liu;Yanan Tian;Tingyang Xu

  • QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles

    Unknown

  • RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions

    Xiaorui Wang;Yuquan Li;Jiezhong Qiu;Guangyong Chen

  • QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine.

    C. X. Xue;Ruisheng Zhang;Huanxiang Liu;Xiaojun Yao

  • Molecular Dynamics Simulation, Free Energy Calculation and Structure-Based 3D-QSAR Studies of B-RAF Kinase Inhibitors

    Ying Yang;Jin Qin;Huanxiang Liu;Xiaojun Yao

  • Molecular modeling study on the resistance mechanism of HCV NS3/4A serine protease mutants R155K, A156V and D168A to TMC435

    Weiwei Xue;Dabo Pan;Ying Yang;Huanxiang Liu

  • Influence of Interface Structure on the Properties of ZnO/Graphene Composites: A Theoretical Study by Density Functional Theory Calculations

    Wei Geng;Xuefei Zhao;Huanxiang Liu;Xiaojun Yao

  • Preparation and In vitro Evaluation of Ethosomal Total Alkaloids of Sophora alopecuroides Loaded by a Transmembrane pH-Gradient Method

    Yan Zhou;Yuhui Wei;Huanxiang Liu;Guoqiang Zhang

  • QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-kappa B mediated gene expression based on support vector machines.

    Huanxiang Liu;Ruisheng Zhang;Xiaojun Yao;Mancang Liu

  • Molecular basis of the interaction for an essential subunit PA-PB1 in influenza virus RNA polymerase: insights from molecular dynamics simulation and free energy calculation.

    Huanxiang Liu;Xiaojun Yao

  • Enhanced photocatalytic properties of titania–graphene nanocomposites: a density functional theory study

    Wei Geng;Huanxiang Liu;Xiaojun Yao

  • Molecular dynamics simulation and free energy calculation studies of the binding mechanism of allosteric inhibitors with p38α MAP kinase.

    Ying Yang;Yulin Shen;Huanxiang Liu;Xiaojun Yao

  • Spectroscopic studies on binding of shikonin to human serum albumin

    Wenying He;Ying Li;Jianniao Tian;Huanxiang Liu

Frequent Co-Authors

Xiaojun Yao
Xiaojun Yao Macau University of Science and Technology
Zhide Hu
Zhide Hu Lanzhou University
Junzhou Huang
Junzhou Huang The University of Texas at Arlington
Roberto Todeschini
Roberto Todeschini University of Milano-Bicocca
Kuo Hsiung Lee
Kuo Hsiung Lee University of Minnesota
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Denis Fourches
Denis Fourches North Carolina State University
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Dragos Horvath
Dragos Horvath University of Strasbourg
Karl-Werner Schramm
Karl-Werner Schramm Technical University of Munich

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