World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
66
Citations
40351
World Ranking
7139
National Ranking
2122

Michael Feig 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 Michael Feig 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: 215 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Michael Feig 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 Michael Feig 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: 66 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2005 - Fellow of Alfred P. Sloan Foundation

Overview

Michael Feig is affiliated with Michigan State University in the United States. Their primary research areas fall within Biochemistry, Genetics, and Molecular Biology, with a particular focus on Molecular Biology.

The researcher has contributed extensively to topics including:

  • Protein Structure and Dynamics
  • Enzyme Structure and Function
  • RNA and protein synthesis mechanisms
  • RNA Research and Splicing
  • RNA modifications and cancer
  • Bacteriophages and microbial interactions
  • Machine Learning in Materials Science

Some recent notable publications by Michael Feig include:

  • "Multi-state modeling of G-protein coupled receptors at experimental accuracy" (2022), published in Proteins Structure Function and Bioinformatics
  • "Direct generation of protein conformational ensembles via machine learning" (2023), published in Nature Communications
  • "Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement" (2020), published in bioRxiv (Cold Spring Harbor Laboratory)
  • "New parallel computing algorithm of molecular dynamics for extremely huge scale biological systems" (2020), published in Journal of Computational Chemistry
  • "CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed" (2024), published in The Journal of Physical Chemistry B

Michael Feig frequently publishes in venues such as:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Biophysical Journal
  • The Journal of Physical Chemistry B
  • Journal of Chemical Theory and Computation
  • Proteins Structure Function and Bioinformatics

The scientist collaborates regularly with several co-authors across multiple studies, including:

  • Lim Heo
  • Lisa J. Lapidus
  • Giacomo Janson
  • Alexander Jussupow
  • Gilberto Valdés-García

In 2005, Michael Feig was recognized as a Fellow of the Alfred P. Sloan Foundation.

Best Publications

  • CHARMM: the biomolecular simulation program.

    B. R. Brooks;C. L. Brooks;A. D. Mackerell;L. Nilsson

  • CHARMM36m: An improved force field for folded and intrinsically disordered proteins

    Jing Huang;Sarah Rauscher;Grzegorz Nawrocki;Ting Ran

  • Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles.

    Robert B. Best;Xiao Zhu;Jihyun Shim;Pedro E. M. Lopes

  • Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations.

    Alexander D. Mackerell;Michael Feig;Charles L. Brooks

  • Improved treatment of the protein backbone in empirical force fields.

    Alexander D. MacKerell;Michael Feig;Charles L. Brooks

  • MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology

    Michael Feig;John Karanicolas;Charles L. Brooks

  • Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures.

    Michael Feig;Alexey Onufriev;Michael S. Lee;Wonpil Im

  • Recent advances in the development and application of implicit solvent models in biomolecule simulations.

    Michael Feig;Charles L Brooks

  • New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations.

    Michael S. Lee;Michael Feig;Freddie R. Salsbury;Charles L. Brooks

  • An Implicit Membrane Generalized Born Theory for the Study of Structure, Stability, and Interactions of Membrane Proteins

    Wonpil Im;Michael Feig;Charles L. Brooks

  • Solvation and hydration of proteins and nucleic acids: a theoretical view of simulation and experiment.

    Vladimir Makarov;B. Montgomery Pettitt;Michael Feig

  • Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

    Isseki Yu;Takaharu Mori;Tadashi Ando;Ryuhei Harada

  • Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008

    Mayako Michino;Enrique Abola;Charles L. Brooks;J. Scott Dixon

  • Multi‐state modeling of G‐protein coupled receptors at experimental accuracy

    Unknown

  • Sodium and Chlorine Ions as Part of the DNA Solvation Shell

    Michael Feig;B. Montgomery Pettitt

  • Force field influence on the observation of π-helical protein structures in molecular dynamics simulations

    Michael Feig;Alexander D. Mackerell;Charles L. Brooks

  • Protein Crowding Affects Hydration Structure and Dynamics

    Ryuhei Harada;Yuji Sugita;Michael Feig;Michael Feig

  • GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

    Jaewoon Jung;Takaharu Mori;Chigusa Kobayashi;Yasuhiro Matsunaga

  • A distinct type of glycerol-3-phosphate acyltransferase with sn-2 preference and phosphatase activity producing 2-monoacylglycerol

    Weili Yang;Mike Pollard;Yonghua Li-Beisson;Fred Beisson

  • A generalized Born formalism for heterogeneous dielectric environments: application to the implicit modeling of biological membranes.

    Seiichiro Tanizaki;Michael Feig

  • Diffusion of solvent around biomolecular solutes: a molecular dynamics simulation study.

    Vladimir A. Makarov;Vladimir A. Makarov;Michael Feig;B. Kim Andrews;B. Montgomery Pettitt

  • Implicit solvation based on generalized Born theory in different dielectric environments.

    Michael Feig;Wonpil Im;Charles L. Brooks

Frequent Co-Authors

Yuji Sugita
Yuji Sugita RIKEN Center for Biosystems Dynamics Research
Charles L. Brooks
Charles L. Brooks University of Michigan–Ann Arbor
Alexander D. MacKerell
Alexander D. MacKerell University of Maryland, Baltimore
B. Montgomery Pettitt
B. Montgomery Pettitt The University of Texas Medical Branch at Galveston
Wonpil Im
Wonpil Im Lehigh University
Vladimir Makarov
Vladimir Makarov Memorial Sloan Kettering Cancer Center
Guo-Wei Wei
Guo-Wei Wei Michigan State University
Robert P. Hausinger
Robert P. Hausinger Michigan State University
Helmut Grubmüller
Helmut Grubmüller Max Planck Society
Hui Li
Hui Li Beijing University of Chemical Technology

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