World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
10858
World Ranking
9085
National Ranking
219

Martin Weigt publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Martin Weigt sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 119 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Martin Weigt D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Martin Weigt sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Martin Weigt is affiliated with Sorbonne University in France and has contributed extensively to the fields of biochemistry, genetics, and molecular biology. Their research covers a range of subfields, including molecular biology, genetics, infectious diseases, ecology, and radiology, nuclear medicine, and imaging.

The scientist's work engages with several core topics in biological sciences, focusing on RNA and protein synthesis mechanisms, genomics and phylogenetic studies, protein structure and dynamics, evolution and genetic dynamics, machine learning applications in bioinformatics, bioinformatics and genomic networks, as well as vaccines and immunoinformatics approaches.

Martin Weigt's notable recent papers include:

  • "An evolution-based model for designing chorismate mutase enzymes" (2020, Science)
  • "Efficient generative modeling of protein sequences using simple autoregressive models" (2021, Nature Communications)
  • "Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes" (2022, Proceedings of the National Academy of Sciences)
  • "Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution" (2022, IRIS Research product catalog, Sapienza University of Rome)
  • "TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes" (2024, Proceedings of the National Academy of Sciences)

Frequent collaborators in the scientist's body of work include Francesco Zamponi, Andrea Pagnani, Juan Rodriguez-Rivas, Giancarlo Croce, and Philippe Nghe. These collaborations have contributed to advancing knowledge in the intersecting domains of protein evolution, genomics, and computational biology.

Martin Weigt has published prolifically in multiple venues, with a significant number of publications appearing in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Nature Communications
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the National Academy of Sciences

Best Publications

  • Direct-coupling analysis of residue coevolution captures native contacts across many protein families

    Faruck Morcos;Andrea Pagnani;Bryan Lunt;Arianna Bertolino

  • On the properties of small-world network models

    A. Barrat;M. Weigt

  • Identification of direct residue contacts in protein-protein interaction by message passing.

    Martin Weigt;Robert A. White;Hendrik Szurmant;James A. Hoch

  • An evolution-based model for designing chorismate mutase enzymes.

    William P. Russ;Matteo Figliuzzi;Christian Stocker;Pierre Barrat-Charlaix;Pierre Barrat-Charlaix

  • Inverse statistical physics of protein sequences: a key issues review.

    Simona Cocco;Christoph Feinauer;Matteo Figliuzzi;Rémi Monasson

  • Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

    Matteo Figliuzzi;Hervé Jacquier;Alexander Schug;Olivier Tenaillon

  • Genomics-aided structure prediction

    Joanna I. Sułkowska;Faruck Morcos;Martin Weigt;Terence Hwa

  • Coloring random graphs.

    Roberto Mulet;Andrea Pagnani;Martin Weigt;Riccardo Zecchina

  • High-resolution protein complexes from integrating genomic information with molecular simulation.

    Alexander Schug;Martin Weigt;José N. Onuchic;Terence Hwa

  • Number of guards needed by a museum: a phase transition in vertex covering of random graphs.

    Martin Weigt;Alexander K. Hartmann

  • Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Evan J. Molinelli;Evan J. Molinelli;Anil Korkut;Weiqing Wang;Martin L. Miller

  • Clustering by soft-constraint affinity propagation

    Michele Leone;Sumedha;Martin Weigt

  • How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins

    Matteo Figliuzzi;Pierre Barrat-Charlaix;Martin Weigt

  • Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

    Eleonora De Leonardis;Eleonora De Leonardis;Benjamin Lutz;Sebastian Ratz;Simona Cocco

  • Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

    Carlo Baldassi;Marco Zamparo;Christoph Feinauer;Andrea Procaccini

  • A variational description of the ground state structure in random satisfiability problems

    Giulio Biroli;Rémi Monasson;Martin Weigt

  • From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.

    Simona Cocco;Remi Monasson;Martin Weigt

  • Simplest random K-satisfiability problem.

    Federico Ricci-Tersenghi;Martin Weigt;Riccardo Zecchina

  • Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

    Thomas Gueudré;Carlo Baldassi;Marco Zamparo;Martin Weigt

  • Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

    Guido Uguzzoni;Shalini John Lovis;Francesco Oteri;Alexander Schug

Frequent Co-Authors

Terence Hwa
Terence Hwa University of California, San Diego
José N. Onuchic
José N. Onuchic Rice University
James A. Hoch
James A. Hoch Scripps Research Institute
Chris Sander
Chris Sander Harvard University
Olivier Tenaillon
Olivier Tenaillon Université Paris Cité
Rama Ranganathan
Rama Ranganathan University of Chicago
Claude Thermes
Claude Thermes University of Paris-Saclay
Alain Barrat
Alain Barrat Centre de Physique Théorique
Debora S. Marks
Debora S. Marks Harvard University
Donald Hilvert
Donald Hilvert ETH Zurich

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to several related online degrees and diverse career paths. Many students are exploring the fastest computer science degree options to quickly gain industry-ready skills and enter the tech workforce sooner. These accelerated programs are designed for individuals seeking an intensive, flexible learning experience.

If your interests intersect with technology and sustainability, consider an environmental engineering online degree. This pathway prepares graduates to develop impactful solutions for environmental challenges—combining engineering fundamentals with environmental science.

Another popular choice is an online mechanical engineering degree, which emphasizes mechanical systems, robotics, and manufacturing. This degree can lead to a broad range of engineering careers in both private and public sectors.

For students with a passion for science and analytics, an online bachelor's degree in physics offers the flexibility to study fundamental scientific principles and prepares graduates for roles in research, education, or technology.

Best Scientists Citing Martin Weigt

Trending Scientists

Recently Published Articles