World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
96
Citations
35590
World Ranking
1536
National Ranking
590

Biology and Biochemistry

D-Index
100
Citations
38771
World Ranking
1505
National Ranking
849

Research.com Recognitions

  • 2001 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 1983 - Fellow of Alfred P. Sloan Foundation

Overview

Jeffrey Skolnick is affiliated with the Georgia Institute of Technology in the United States. Their research focuses predominantly on the fields of Biochemistry, Genetics, and Molecular Biology, with a significant concentration in Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Infectious Diseases, and Genetics.

Their main areas of work include:

  • Protein Structure and Dynamics
  • Computational Drug Discovery Methods
  • Machine Learning in Bioinformatics
  • RNA and protein synthesis mechanisms
  • Bioinformatics and Genomic Networks
  • Metabolomics and Mass Spectrometry Studies
  • Machine Learning in Materials Science

Frequent collaborators include Mu Gao, Hongyi Zhou, Jianlin Cheng, and Davi Nakajima An.

The most common venues for their publications are:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Scientific Reports
  • The Journal of Physical Chemistry B
  • Journal of Chemical Information and Modeling

Recent notable publications by Jeffrey Skolnick include:

  • AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function, 2021, Journal of Chemical Information and Modeling
  • AF2Complex predicts direct physical interactions in multimeric proteins with deep learning, 2022, Nature Communications
  • Protein folds vs. protein folding: Differing questions, different challenges, 2022, Proceedings of the National Academy of Sciences
  • Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks, 2022, Nature Communications
  • Antimalarial Peptide and Polyketide Natural Products from the Fijian Marine Cyanobacterium Moorea producens, 2020, Marine Drugs

Jeffrey Skolnick has been recognized as a Fellow of the American Association for the Advancement of Science (AAAS) since 2001. Earlier in their career, they were also a Fellow of the Alfred P. Sloan Foundation in 1983.

Best Publications

  • TM-align: a protein structure alignment algorithm based on the TM-score.

    Yang Zhang;Jeffrey Skolnick

  • Scoring function for automated assessment of protein structure template quality

    Yang Zhang;Jeffrey Skolnick

  • From genes to protein structure and function: novel applications of computational approaches in the genomic era

    Jeffrey Skolnick;Jacquelyn S. Fetrow

  • Electrostatic Persistence Length of a Wormlike Polyelectrolyte

    Jeffrey Skolnick;Marshall Fixman

  • Ab initio modeling of small proteins by iterative TASSER simulations

    Sitao Wu;Jeffrey Skolnick;Yang Zhang

  • How well is enzyme function conserved as a function of pairwise sequence identity

    Weidong Tian;Jeffrey Skolnick

  • Fast procedure for reconstruction of full-atom protein models from reduced representations

    Piotr Rotkiewicz;Jeffrey Skolnick

  • Topology fingerprint approach to the inverse protein folding problem.

    Adam Godzik;Andrzej Kolinski;Andrzej Kolinski;Jeffrey Skolnick

  • SPICKER: A clustering approach to identify near‐native protein folds

    Yang Zhang;Jeffrey Skolnick

  • Crowding and hydrodynamic interactions likely dominate in vivo macromolecular motion

    Tadashi Ando;Jeffrey Skolnick

  • The protein structure prediction problem could be solved using the current PDB library.

    Yang Zhang;Jeffrey Skolnick

  • Automated structure prediction of weakly homologous proteins on a genomic scale

    Yang Zhang;Jeffrey Skolnick

  • Simulations of the folding of a globular protein

    Jeffrey Skolnick;Andrzej Kolinski

  • A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

    Michal Brylinski;Jeffrey Skolnick

  • Monte carlo simulations of protein folding. I. Lattice model and interaction scheme

    Andrzej Kolinski;Andrzej Kolinski;Jeffrey Skolnick

  • AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

    Unknown

  • A distance-dependent atomic knowledge-based potential for improved protein structure selection

    Hui Lu;Jeffrey Skolnick

  • TOUCHSTONE II: a new approach to ab initio protein structure prediction.

    Yang Zhang;Andrzej Kolinski;Andrzej Kolinski;Jeffrey Skolnick

  • MULTIPROSPECTOR: An algorithm for the prediction of protein–protein interactions by multimeric threading

    Long Lu;Long Lu;Hui Lu;Jeffrey Skolnick

  • What is the probability of a chance prediction of a protein structure with an rmsd of 6 å

    Boris A Reva;Alexei V Finkelstein;Jeffrey Skolnick

  • MONSSTER: a method for folding globular proteins with a small number of distance restraints

    Jeffrey Skolnick;Andrzej Kolinski;Andrzej Kolinski;Angel R. Ortiz

  • Erratum: Scoring function for automated assessment of protein structure template quality (Proteins: Structure, Function and Genetics (2004) 57, (702-710))

    Yang Zhang;Jeffrey Skolnick

Frequent Co-Authors

Andrzej Kolinski
Andrzej Kolinski University of Warsaw
Yang Zhang
Yang Zhang University of Michigan–Ann Arbor
Adam Godzik
Adam Godzik University of California, Riverside
Daisuke Kihara
Daisuke Kihara Purdue University West Lafayette
Charles L. Brooks
Charles L. Brooks University of Michigan–Ann Arbor
Eugene I. Shakhnovich
Eugene I. Shakhnovich Harvard University
John F. McDonald
John F. McDonald Georgia Institute of Technology
Yousef Saad
Yousef Saad University of Minnesota
Eugene Helfand
Eugene Helfand Nokia (United States)
Gaetano T. Montelione
Gaetano T. Montelione Rensselaer Polytechnic Institute

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