World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
51
Citations
8816
World Ranking
14004
National Ranking
3629

Marc C. Nicklaus 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 Marc C. Nicklaus 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: 191 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Marc C. Nicklaus 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 Marc C. Nicklaus 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: 51 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Marc C. Nicklaus is affiliated with the National Institutes of Health in the United States, focusing their research primarily at the intersection of computer science and biochemistry, genetics, and molecular biology. Their scholarly output reflects a strong emphasis on computational methods in drug discovery and materials science, with notable contributions across related subfields.

Their main fields of study include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Within these areas, their research delves into specialized subfields such as:

  • Computational Theory and Mathematics
  • Molecular Biology
  • Materials Chemistry
  • Radiology, Nuclear Medicine and Imaging
  • Physical and Theoretical Chemistry

Marc C. Nicklaus' work spans multiple topics, notably:

  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Chemical Synthesis and Analysis
  • Monoclonal and Polyclonal Antibodies Research
  • Analytical Chemistry and Chromatography
  • Innovative Microfluidic and Catalytic Techniques Innovation
  • Tuberculosis Research and Epidemiology

Their publication record includes a significant number of articles in recurrent scientific venues, exemplified by the following frequent publication sources:

  • Journal of Chemical Information and Modeling
  • Scientific Data
  • Journal of Cheminformatics
  • Research Square (Research Square)
  • UNC Libraries

Selected recent publications demonstrate a focus on chemical informatics and large-scale computational approaches in drug discovery and toxicity modeling. These papers include:

  • "Exploration of Ultralarge Compound Collections for Drug Discovery," 2022, Journal of Chemical Information and Modeling
  • "Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods," 2021, Journal of Chemical Information and Modeling
  • "SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules," 2020, Scientific Data
  • "Toward a Comprehensive Treatment of Tautomerism in Chemoinformatics Including in InChI V2," 2020, Journal of Chemical Information and Modeling
  • "Tautomer Database: A Comprehensive Resource for Tautomerism Analyses," 2020, Journal of Chemical Information and Modeling

Collaborative efforts have featured several frequent coauthors, illustrating the connected nature of this research. Notable frequent collaborators include:

  • Nadya I. Tarasova
  • Victorien Delannée
  • Philip N. Judson
  • Matthias Rarey
  • Hitesh Patel

Best Publications

  • CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

    Kamel Mansouri;Ahmed Abdelaziz;Aleksandra Rybacka;Alessandra Roncaglioni

  • Depsides and depsidones as inhibitors of HIV-1 integrase: discovery of novel inhibitors through 3D database searching.

    Nouri Neamati;Huixiao Hong;Abhijit Mazumder;Shaomeng Wang

  • PASS Biological Activity Spectrum Predictions in the Enhanced Open NCI Database Browser

    Vladimir V. Poroikov;Dmitrii A. Filimonov;# Wolf-Dietrich Ihlenfeldt;Tatyana A. Gloriozova

  • Conformational changes of small molecules binding to proteins

    Marc C. Nicklaus;Shaomeng Wang;John S. Driscoll;George W.A. Milne

  • Comparison of the NCI open database with seven large chemical structural databases.

    Johannes H. Voigt;Bruno Bienfait;Shaomeng Wang;Marc C. Nicklaus

  • Structure activity of 3-aryl-1,3-diketo-containing compounds as HIV-1 integrase inhibitors.

    Godwin C. G. Pais;Xuechun Zhang;Christophe Marchand;Nouri Neamati

  • Optical Structure Recognition Software To Recover Chemical Information: OSRA — An Open Source Solution

    Igor V. Filippov;Marc C. Nicklaus

  • Comparison of Nine Programs Predicting pKa Values of Pharmaceutical Substances

    Chenzhong Liao;Marc C. Nicklaus

  • HIV-1 integrase pharmacophore: discovery of inhibitors through three-dimensional database searching.

    Marc C. Nicklaus;Nouri Neamati;Huixiao Hong;Abhijit Mazumder

  • Antiretroviral agents as inhibitors of both human immunodeficiency virus type 1 integrase and protease.

    Abhijit Mazumder;Shaomeng Wang;Nouri Neamati;Marc Nicklaus

  • Structural and functional analyses of minimal phosphopeptides targeting the polo-box domain of polo-like kinase 1

    Sang-Moon Yun;Tinoush Moulaei;Dan Lim;Jeong K. Bang

  • Software and resources for computational medicinal chemistry

    Chenzhong Liao;Markus Sitzmann;Angelo Pugliese;Marc C Nicklaus

  • Exploration of Ultralarge Compound Collections for Drug Discovery

    Unknown

  • Metal-Dependent Inhibition of HIV-1 Integrase by β-Diketo Acids and Resistance of the Soluble Double-Mutant (F185K/C280S)

    Christophe Marchand;Allison A. Johnson;Rajeshri G. Karki;Godwin C. G. Pais

  • National Cancer Institute Drug Information System 3D database.

    George W. A. Milne;Marc C. Nicklaus;John S. Driscoll;Shaomeng Wang

  • Discovery of novel, non-peptide HIV-1 protease inhibitors by pharmacophore searching

    Shaomeng Wang;G. W. A. Milne;Xinjian Yan;Isadora J. Posey

  • Discovery of HIV-1 integrase inhibitors by pharmacophore searching.

    Huixiao Hong;Nouri Neamati;Shaomeng Wang;Marc C. Nicklaus

  • Experimental and structural evidence that herpes 1 kinase and cellular DNA polymerase(s) discriminate on the basis of sugar pucker.

    Victor E. Marquez;Tsipi Ben-Kasus;Joseph J. Barchi;Karen M. Green

  • Conformationally locked nucleoside analogues. Synthesis of dideoxycarbocyclic nucleoside analogues structurally related to neplanocin C

    Juan B. Rodriguez;Victor E. Marquez;Marc C. Nicklaus;Hiroaki Mitsuya

  • Enhanced CACTVS browser of the Open NCI Database.

    Wolf-Dietrich Ihlenfeldt;Johannes H. Voigt;Bruno Bienfait;Frank Oellien

  • QSAR modeling of imbalanced high-throughput screening data in PubChem.

    Alexey V. Zakharov;Megan L. Peach;Markus Sitzmann;Marc C. Nicklaus

  • Effects of tyrphostins, protein kinase inhibitors, on human immunodeficiency virus type 1 integrase.

    Abhijit Mazumder;Aviv Gazit;Alexander Levitzki;Marc Nicklaus

Frequent Co-Authors

Victor E. Marquez
Victor E. Marquez National Institutes of Health
Terrence R. Burke
Terrence R. Burke National Institutes of Health
Yves Pommier
Yves Pommier National Institutes of Health
Vladimir Poroikov
Vladimir Poroikov Institute of Business & Medical Careers
Shaomeng Wang
Shaomeng Wang University of Michigan–Ann Arbor
Robert J. Fisher
Robert J. Fisher Science Applications International Corporation (United States)
Christophe Marchand
Christophe Marchand National Institutes of Health
Peter P. Roller
Peter P. Roller National Institutes of Health
Peter M. Blumberg
Peter M. Blumberg National Institutes of Health
Vinay K. Pathak
Vinay K. Pathak National Institutes of Health

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