World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
60
Citations
13257
World Ranking
9671
National Ranking
32

Adam Liwo 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 Adam Liwo 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.

Adam Liwo 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 Adam Liwo 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: 60 D-Index — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Overview

Adam Liwo is affiliated with the University of Gdańsk in Poland and has contributed extensively to research in biochemistry, genetics, and molecular biology, with additional work in materials science. Their research primarily focuses on molecular biology and materials chemistry, with interests also spanning spectroscopy, atomic and molecular physics, and ecology.

Their main areas of investigation include:

  • Protein Structure and Dynamics
  • Enzyme Structure and Function
  • RNA and protein synthesis mechanisms
  • Bacteriophages and microbial interactions
  • Mass Spectrometry Techniques and Applications
  • Advanced NMR Techniques and Applications
  • Machine Learning in Materials Science

Frequently publishing in specialized venues, Adam Liwo has contributed multiple articles to:

  • Journal of Computational Chemistry
  • The Journal of Physical Chemistry B
  • Proteins Structure Function and Bioinformatics
  • Journal of Chemical Theory and Computation
  • Biomolecules

Their recent publications include:

  • "Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems," 2021, Biomolecules
  • "Pragmatic Coarse-Graining of Proteins: Models and Applications," 2023, Journal of Chemical Theory and Computation
  • "Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment," 2023, Proteins Structure Function and Bioinformatics
  • "Protein folds vs. protein folding: Differing questions, different challenges," 2022, Proceedings of the National Academy of Sciences
  • "Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment," 2021, Proteins Structure Function and Bioinformatics

Adam Liwo has collaborated frequently with several researchers, including:

  • Cezary Czaplewski
  • Adam K. Sieradzan
  • Emilia A. Lubecka
  • Agnieszka G. Lipska
  • Artur Giełdoń

Beyond journal articles, they have contributed to the book "Innovation in Physical Activity and Sport," published by Springer International Publishing in 2021.

Best Publications

  • Protein-Folding Dynamics: Overview of Molecular Simulation Techniques

    Harold A. Scheraga;Mey Khalili;Adam Liwo

  • Principal Component Analysis for Protein Folding Dynamics

    Gia G. Maisuradze;Adam Liwo;Harold A. Scheraga

  • A united-residue force field for off-lattice protein-structure simulations. I. Functional forms and parameters of long-range side-chain interaction potentials from protein crystal data

    Adam Liwo;Adam Liwo;Stanislaw Oldziej;Matthew R. Pincus;Ryszard J. Wawak

  • Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains.

    Adam Liwo;Mey Khalili;Harold A. Scheraga

  • Relation between free energy landscapes of proteins and dynamics.

    Gia G. Maisuradze;Adam Liwo;Harold A. Scheraga

  • Cumulant-based expressions for the multibody terms for the correlation between local and electrostatic interactions in the united-residue force field

    Adam Liwo;Cezary Czaplewski;Jarosław Pillardy;Harold A. Scheraga

  • Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins.

    Adam Liwo;Mey Khalili;Cezary Czaplewski;Sebastian Kalinowski

  • A united‐residue force field for off‐lattice protein‐structure simulations. II. Parameterization of short‐range interactions and determination of weights of energy terms by Z‐score optimization

    Adam Liwo;Adam Liwo;Matthew R. Pincus;Ryszard J. Wawak;Shelly Rackovsky

  • Protein structure prediction by global optimization of a potential energy function

    Adam Liwo;Jooyoung Lee;Daniel R. Ripoll;Jaroslaw Pillardy

  • United-residue force field for off-lattice protein-structure simulations: III. Origin of backbone hydrogen-bonding cooperativity in united-residue potentials

    Adam Liwo;Adam Liwo;Adam Liwo;Rajmund Kazmierkiewicz;Cezary Czaplewski;Malgorzata Groth

  • Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: Application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K

    Jooyoung Lee;Adam Liwo;Harold A. Scheraga

  • Computational techniques for efficient conformational sampling of proteins.

    Adam Liwo;Cezary Czaplewski;Stanisław Ołdziej;Stanisław Ołdziej;Harold A Scheraga

  • Recent improvements in prediction of protein structure by global optimization of a potential energy function.

    Jarosław Pillardy;Cezary Czaplewski;Adam Liwo;Jooyoung Lee

  • Polyproline II conformation is one of many local conformational states and is not an overall conformation of unfolded peptides and proteins

    Joanna Makowska;Sylwia Rodziewicz-Motowidło;Katarzyna Bagińska;Jorge A. Vila

  • Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

    S. Ołdziej;C. Czaplewski;A. Liwo;M. Chinchio

  • Molecular Dynamics with the United-Residue Model of Polypeptide Chains. II. Langevin and Berendsen-Bath Dynamics and Tests on Model α-Helical Systems

    Mey Khalili;Adam Liwo;and Anna Jagielska;Harold A. Scheraga

  • Prediction of protein conformation on the basis of a search for compact structures: Test on avian pancreatic polypeptide

    A. Liwo;M. R. Pincus;R. J. Wawak;S. Rackovsky

  • A unified coarse-grained model of biological macromolecules based on mean-field multipole–multipole interactions

    Adam Liwo;Maciej Baranowski;Cezary Czaplewski;Ewa Gołaś;Ewa Gołaś

  • A general method for the determination of the stoichiometry of unknown species in multicomponent systems from physicochemical measurements

    Jaroslaw Kostrowicki;Adam Liwo

  • A method for optimizing potential-energy functions by a hierarchical design of the potential-energy landscape: application to the UNRES force field.

    Adam Liwo;Piotr Arłukowicz;Cezary Czaplewski;Stanisław Ołdziej

Frequent Co-Authors

Harold A. Scheraga
Harold A. Scheraga Cornell University
Paul A. Janmey
Paul A. Janmey University of Pennsylvania
Salvador Ventura
Salvador Ventura Autonomous University of Barcelona
Juri Rappsilber
Juri Rappsilber Technical University of Berlin
Jianlin Cheng
Jianlin Cheng University of Missouri
Jerrold Meinwald
Jerrold Meinwald Cornell University
David Baker
David Baker University of Washington
Andras Fiser
Andras Fiser Albert Einstein College of Medicine

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