World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
54
Citations
13729
World Ranking
12462
National Ranking
3321

Yingkai Zhang 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 Yingkai Zhang 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: 131 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Yingkai Zhang 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 Yingkai Zhang 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: 54 D-Index — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Overview

Yingkai Zhang is a researcher affiliated with New York University in the United States. Their work spans multiple disciplines with a significant focus on biochemistry, genetics, and molecular biology, as well as contributions to medicine and computer science.

The main fields of study for this scientist include:

  • Biochemistry, Genetics and Molecular Biology
  • Medicine
  • Computer Science

Within these broader areas, Zhang's research delves into several subfields, notably molecular biology, computational theory and mathematics, materials chemistry, oncology, and pharmacology.

  • Molecular Biology
  • Computational Theory and Mathematics
  • Materials Chemistry
  • Oncology
  • Pharmacology

Zhang has contributed to research on diverse topics, including computational drug discovery methods, protein structure and dynamics, machine learning applications in materials science, chemical synthesis and analysis, studies of protein tyrosine phosphatases, mechanisms of RNA and protein synthesis, and ubiquitin and proteasome pathways.

  • Computational Drug Discovery Methods
  • Protein Structure and Dynamics
  • Machine Learning in Materials Science
  • Chemical Synthesis and Analysis
  • Protein Tyrosine Phosphatases
  • RNA and protein synthesis mechanisms
  • Ubiquitin and proteasome pathways

Frequent co-authors collaborating with Zhang include:

  • Chao Yang
  • Xuben Hou
  • William M. Marsiglia
  • Jing Zhao
  • Xinyue Liu

Zhang's work has appeared in multiple publication venues, with the highest number of contributions to the Journal of Chemical Information and Modeling, followed by Nature Communications and the Journal of Chemical Theory and Computation. Zhang has also published in the Journal of Ethnopharmacology and archives such as arXiv (Cornell University).

  • Journal of Chemical Information and Modeling
  • Nature Communications
  • Journal of Chemical Theory and Computation
  • Journal of Ethnopharmacology
  • arXiv (Cornell University)

Notable recent papers authored or co-authored by Yingkai Zhang include:

  • "Targeting Amyloidogenic Processing of APP in Alzheimer's Disease" (2020), Frontiers in Molecular Neuroscience
  • "EGCG binds intrinsically disordered N-terminal domain of p53 and disrupts p53-MDM2 interaction" (2021), Nature Communications
  • "Protein-Ligand Docking in the Machine-Learning Era" (2022), Molecules
  • "Unified Deep Learning Model for Multitask Reaction Predictions with Explanation" (2022), Journal of Chemical Information and Modeling
  • "Selective and noncovalent targeting of RAS mutants for inhibition and degradation" (2021), Nature Communications

Best Publications

  • Comment on “Generalized Gradient Approximation Made Simple”

    Yingkai Zhang;Weitao Yang

  • A challenge for density functionals: Self-interaction error increases for systems with a noninteger number of electrons

    Yingkai Zhang;Weitao Yang

  • A pseudobond approach to combining quantum mechanical and molecular mechanical methods

    Yingkai Zhang;Tai Sung Lee;Weitao Yang

  • Free energy calculation on enzyme reactions with an efficient iterative procedure to determine minimum energy paths on a combined ab initio QM/MM potential energy surface

    Yingkai Zhang;Haiyan Liu;Weitao Yang

  • Degenerate Ground States and a Fractional Number of Electrons in Density and Reduced Density Matrix Functional Theory

    Weitao Yang;Yingkai Zhang;Paul W. Ayers

  • Describing van der Waals interaction in diatomic molecules with generalized gradient approximations: The role of the exchange functional

    Yingkai Zhang;Wei Pan;Weitao Yang;Weitao Yang

  • Role of the Catalytic Triad and Oxyanion Hole in Acetylcholinesterase Catalysis: An ab initio QM/MM Study

    Yingkai Zhang;Jeremy Kua;J. Andrew McCammon

  • Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.

    Cheng Wang;Yingkai Zhang;Yingkai Zhang

  • Reaction mechanism of monoethanolamine with CO2 in aqueous solution from molecular modeling

    Hong Bin Xie;Yanzi Zhou;Yingkai Zhang;J. Karl Johnson

  • Improved pseudobonds for combined ab initio quantum mechanical/molecular mechanical methods

    Yingkai Zhang

  • Polaron-to-Polaron Transitions in the Radio-Frequency Spectrum of a Quasi-Two-Dimensional Fermi Gas

    Y. Zhang;Y. Zhang;W. Ong;W. Ong;I. Arakelyan;I. Arakelyan;J. E. Thomas

  • Pseudobond ab initio QM/MM approach and its applications to enzyme reactions

    Yingkai Zhang

  • How does the cAMP-dependent protein kinase catalyze the phosphorylation reaction: an ab initio QM/MM study.

    Yuhui Cheng;Yingkai Zhang;J. Andrew McCammon

  • Density-based energy decomposition analysis for intermolecular interactions with variationally determined intermediate state energies

    Qin Wu;Paul W. Ayers;Yingkai Zhang

  • Protein–Ligand Docking in the Machine-Learning Era

    Unknown

  • A Proton-Shuttle Reaction Mechanism for Histone Deacetylase 8 and the Catalytic Role of Metal Ions

    Ruibo Wu;Shenglong Wang;Nengjie Zhou;Zexing Cao

  • Perspective on “Density-functional theory for fractional particle number: derivative discontinuities of the energy”

    Yingkai Zhang;Weitao Yang

  • Catalytic Reaction Mechanism of Acetylcholinesterase Determined by Born-Oppenheimer ab initio QM/MM Molecular Dynamics Simulations

    Yanzi Zhou;Shenglong Wang;Yingkai Zhang

  • Targeting Amyloidogenic Processing of APP in Alzheimer’s Disease

    Jing Zhao;Xinyue Liu;Weiming Xia;Weiming Xia;Yingkai Zhang

  • A Water-Mediated and Substrate-Assisted Catalytic Mechanism for Sulfolobus solfataricus DNA Polymerase IV

    Lihua Wang;Xinyun Yu;Po Hu;Suse Broyde

  • Studying enzyme binding specificity in acetylcholinesterase using a combined molecular dynamics and multiple docking approach.

    Jeremy Kua;Yingkai Zhang;J. Andrew McCammon

  • EGCG binds intrinsically disordered N-terminal domain of p53 and disrupts p53-MDM2 interaction

    Jing Zhao;Jing Zhao;Alan Blayney;Xiaorong Liu;Lauren Gandy

  • An efficient linear scaling method for ab initio calculation of electron density of proteins

    Ai M. Gao;Da W. Zhang;John Z.H. Zhang;Yingkai Zhang

Frequent Co-Authors

Suse Broyde
Suse Broyde New York University
Weitao Yang
Weitao Yang Duke University
Nicholas E. Geacintov
Nicholas E. Geacintov New York University
J. Andrew McCammon
J. Andrew McCammon University of California, San Diego
John Z. H. Zhang
John Z. H. Zhang New York University
Moosa Mohammadi
Moosa Mohammadi New York University Langone Medical Center
Daiqian Xie
Daiqian Xie Nanjing University
Hua Guo
Hua Guo University of New Mexico
Xin Gao
Xin Gao King Abdullah University of Science and Technology
Xuhui Huang
Xuhui Huang University of Wisconsin–Madison

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