World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
10721
World Ranking
3695
National Ranking
1762

Overview

Daisuke Kihara is affiliated with Purdue University West Lafayette in the United States and has contributed extensively to the field of biochemistry, genetics, and molecular biology. Their research primarily focuses on the molecular biology and structural biology subfields, with significant involvement in materials chemistry, computational theory and mathematics, and computer vision and pattern recognition.

The scientist's work covers several main topics, including protein structure and dynamics, enzyme structure and function, advanced electron microscopy techniques and applications, RNA and protein synthesis mechanisms, machine learning in bioinformatics, genomics and phylogenetic studies, and computational drug discovery methods.

Frequent publication venues for Daisuke Kihara include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Biophysical Journal
  • Methods in molecular biology
  • arXiv (Cornell University)

Among recent papers, several notable publications are:

  • Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment, 2021, Proteins Structure Function and Bioinformatics
  • Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge, 2021, Nature Methods
  • LZerD webserver for pairwise and multiple protein-protein docking, 2021, Nucleic Acids Research
  • Protein folds vs. protein folding: Differing questions, different challenges, 2022, Proceedings of the National Academy of Sciences
  • Protein Docking Model Evaluation by Graph Neural Networks, 2021, Frontiers in Molecular Biosciences

Daisuke Kihara has collaborated frequently with several coauthors, including:

  • Genki Terashi
  • Xiao Wang
  • Charles Christoffer
  • Yuki Kagaya
  • Tunde Aderinwale

Best Publications

  • A large-scale evaluation of computational protein function prediction

    Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • Limitations and potentials of current motif discovery algorithms

    Jianjun Hu;Bin Li;Daisuke Kihara

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T Clark;Asma R Bankapur

  • Protein-protein docking using region-based 3D Zernike descriptors

    Vishwesh Venkatraman;Yifeng D Yang;Lee Sael;Daisuke Kihara

  • Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm.

    Jeffrey Skolnick;Daisuke Kihara;Yang Zhang

  • Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding.

    Yang Zhang;Daisuke Kihara;Jeffrey Skolnick

  • Enhanced automated function prediction using distantly related sequences and contextual association by PFP

    Troy Hawkins;Stanislav Luban;Daisuke Kihara

  • Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

    Marc F. Lensink;Sameer Velankar;Andriy Kryshtafovych;Shen You Huang

  • PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

    Troy Hawkins;Meghana Chitale;Stanislav Luban;Daisuke Kihara

  • Defrosting the frozen approximation: PROSPECTOR--a new approach to threading.

    Jeffrey Skolnick;Daisuke Kihara

  • Community-wide assessment of protein-interface modeling suggests improvements to design methodology

    Sarel J. Fleishman;Sarel J. Fleishman;Timothy A. Whitehead;Eva Maria Strauch;Jacob E. Corn;Jacob E. Corn

  • Fast protein tertiary structure retrieval based on global surface shape similarity.

    Lee Sael;Bin Li;David La;Yi Fang

  • De novo main-chain modeling for EM maps using MAINMAST

    Genki Terashi;Daisuke Kihara

  • Function prediction of uncharacterized proteins.

    Troy Hawkins;Daisuke Kihara

  • Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

    Marc F. Lensink;Guillaume Brysbaert;Nurul Nadzirin;Sameer Velankar

  • ESG: Extended Similarity Group method for automated protein function prediction

    Meghana Chitale;Troy Hawkins;Changsoon Park;Daisuke Kihara

  • ESG: extended similarity group method for automated protein function prediction

    Meghana Chitale;Troy Hawkins;Changsoon Park;Daisuke Kihara

  • Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

    Marc F Lensink;Guillaume Brysbaert;Théo Mauri;Nurul Nadzirin

  • Potential for Protein Surface Shape Analysis Using Spherical Harmonics and 3D Zernike Descriptors

    Vishwesh Venkatraman;Lee Sael;Daisuke Kihara

  • Protein docking model evaluation by 3D deep convolutional neural networks.

    Xiao Wang;Genki Terashi;Charles W Christoffer;Mengmeng Zhu

  • Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

Frequent Co-Authors

Jeffrey Skolnick
Jeffrey Skolnick Georgia Institute of Technology
Jianlin Cheng
Jianlin Cheng University of Missouri
Juan Fernández-Recio
Juan Fernández-Recio Spanish National Research Council
Christophe Dessimoz
Christophe Dessimoz University College London
Shoshana J. Wodak
Shoshana J. Wodak Vrije Universiteit Brussel
Paul A. Bates
Paul A. Bates The Francis Crick Institute
Tapio Salakoski
Tapio Salakoski University of Turku
David T. Jones
David T. Jones University College London
Yang Zhang
Yang Zhang University of Michigan–Ann Arbor
Jeffrey J. Gray
Jeffrey J. Gray Johns Hopkins University

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