Original Papers

 

  1. Furui K, Ohue M.
    Active learning for energy-based antibody optimization and enhanced screening.
    arXiv preprint, doi: 10.48550/arXiv.2409.10964
    arXiv
  2.  

  3. 大上雅史.
    AlphaFold3で何ができる?
    現代化学 2024年7月号, 東京化学同人, 2024.
    Book website
  4.  

  5. Boku T, Sugita M, Kobayashi R, Furuya S, Fujie T, Ohue M, Akiyama Y.
    Improving Performance on Replica-Exchange Molecular Dynamics Simulations by Optimizing GPU Core Utilization.
    In Proceedings of the 53rd International Conference on Parallel Processing (ICPP2024), 1082-1091, 2024. doi: 10.1145/3673038.3673097
    Proceedings website
  6.  

  7. Sakano K, Furui K, Ohue M.
    Natural Product-like Compound Generation\\with Chemical Language Models.
    In Proceedings of The 30th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’24), in press.
  8.  

  9. Kengkanna A, Ohue M.
    Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.
    Communications Chemistry, 7: 74, 2024. doi: 10.1038/s42004-024-01155-w
    Journal website | PubMed | GitHub
  10.  

  11. Ohue M, Sasayama K, Takata M.
    Mathematical Modeling and Problem Solving: From Fundamentals to Applications.
    The Journal of Supercomputing, 80: 14116-14119, 2024. doi: 10.1007/s11227-024-06007-x
    Journal website
  12.  

  13. Furui K, Ohue M.
    FastLomap: Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation.
    The Journal of Supercomputing, 80: 14417-14432, 2024. doi: 10.1007/s11227-024-06006-y
    Journal website | GitHub
  14.  

  15. Ueki T, Ohue M.
    Antibody Complementarity-Determining Region Design using AlphaFold2 and DDG Predictor.
    The Journal of Supercomputing, 80: 11989-12002, 2024. doi: 10.1007/s11227-023-05887-9
    Journal website | GitHub
  16.  

  17. Hu W, Ohue M.
    SpatialPPI: three-dimensional space protein-protein interaction prediction with AlphaFold Multimer.
    Computational and Structural Biotechnology Journal, 23: 1214-1225, 2024. doi: 10.1016/j.csbj.2024.03.009
    Journal website | PubMed | bioRxiv | GitHub
  18.  

  19. Ochiai T, Inukai T, Akiyama M, Furui K, Ohue M, Matsumori N, Inuki S, Uesugi M, Sunazuka T, Kikuchi K, Kakeya H, Sakakibara Y.
    Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity.
    Communications Chemistry, 6: 249, 2023. doi: 10.1038/s42004-023-01054-6
    Journal website | ChemRxiv | PubMed | GitHub
  20.  

  21. 大上雅史.
    AlphaFoldによる高精度なタンパク質立体構造予測と創薬への応用.
    PHARM STAGE, 23: 8, 24-28, 2023.
    Journal website
  22.  

  23. Murakumo K, Yoshikawa N, Rikimaru K, Nakamura S, Furui K, Suzuki T, Yamasaki H, Nishigaya Y, Takagi Y, Ohue M.
    LLM Drug Discovery Challenge: A Contest as a Feasibility Study on the Utilization of Large Language Models in Medicinal Chemistry.
    In Proceedings of AI for Accelerated Materials Design (AI4Mat) NeurIPS 2023 Workshop, 2023.
    OpenReview.net
  24.  

  25. Ohue M.
    MEGADOCK-on-Colab: an easy-to-use protein-protein docking tool on Google Colaboratory.
    BMC Research Notes, 16(1): 229, 2023. doi: 10.1186/s13104-023-06505-w
    Journal website | Jxiv | PubMed | GitHub
  26.  

  27. Kosugi T, Ohue M.
    Design of cyclic peptides targeting protein–protein interactions using AlphaFold.
    International Journal of Molecular Sciences, 24(17): 13257, 2023. doi: 10.3390/ijms241713257
    Journal website | bioRxiv | PubMed | GitHub
  28.  

  29. 大上雅史.
    AlphaFoldによるタンパク質立体構造予測(実践編).
    生物工学会誌 2023年8月号, 101(8): 443-446, 2023. doi: 10.34565/seibutsukogaku.101.8_443
    Journal website
  30.  

  31. Arai M, Makita Y, Saito S, Suzuki S, Narushima Y, Izawa N, Tanaka Y, Furui K, Ohue M, Ishibashi M.
    The Indole Rocaglamide Induces S and G2/M Phase Cell Cycle Arrest in Small Cell Lung Cancer Cells Through ASCL1 Translation Inhibition.
    SSRN preprint, 2023. doi: 10.2139/ssrn.4493242
    Cell Press Sneak Peak
  32.  

  33. Ohue M, Kojima Y, Kosugi T.
    Generating potential protein-protein interaction inhibitor molecules based on physicochemical properties.
    Molecules, 28(15): 5652, 2023. doi: 10.3390/molecules28155652
    Journal website | Preprints.org | PubMed | GitHub
  34.  

  35. 大上雅史.
    AIによって変わる生命科学.
    現代化学 2023年8月号, 28-30, 東京化学同人, 2023.
    Book website
  36.  

  37. Andreani J, Jiménez-García B, Ohue M.
    Web Tools for Modeling and Analysis of Biomolecular Interactions Volume II.
    Frontiers in Molecular Biosciences, 10: 1190855, 2023. doi: 10.3389/fmolb.2023.1190855
    Journal website | PubMed
  38.  

  39. 大上雅史.
    PPI(タンパク質間相互作用)を標的とするドラッグデザイン.
    タンパク質の構造解析手法とIn silicoスクリーニングへの応用事例, 402-410, 技術情報協会, 2023.
    Book website
  40.  

  41. Kengkanna A, Ohue M.
    Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction.
    In Proceedings of The 20th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2023), 8 pages, 2023. doi: 10.1109/CIBCB56990.2023.10264879
    Proceedings website | * Download | arXiv | Presentation video
    * © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  42.  

  43. Ueki T, Ohue M.
    Antibody Complementarity-Determining Region Sequence Design using AlphaFold2 and Binding Affinity Prediction Model.
    In Proceedings of The 29th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’23), 2133-2139, 2023. doi: 10.1109/CSCE60160.2023.00350
    Proceedings website | Download | bioRxiv
  44.  

  45. Furui K, Ohue M.
    Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation.
    In Proceedings of The 29th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’23), 2126-2132, 2023. doi: 10.1109/CSCE60160.2023.00349
    Proceedings website | Download | arXiv | GitHub
  46.  

  47. Li J, Yanagisawa K, Sugita M, Fujie T, Ohue M, Akiyama Y.
    CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides.
    Journal of Chemical Information and Modeling, 63(7): 2240-2250, 2023. doi: 10.1021/acs.jcim.2c01573
    Journal website | PubMed | Database
  48.  

  49. 大上雅史.
    中分子ペプチド創薬のインフォマティクス.
    実験医学, 2023年1月号, 41(1): 20-26, 羊土社, 2023. doi: 10.18958/7173-00001-0000342-00
    Book website
  50.  

  51. Sugita M, Fujie T, Yanagisawa K, Ohue M, Akiyama Y.
    Lipid composition is critical for accurate membrane permeability prediction of cyclic peptides by molecular dynamics simulations.
    Journal of Chemical Information and Modeling, 62(18): 4549-4560, 2022. doi: 10.1021/acs.jcim.2c00931
    Journal website | PubMed
  52.  

  53. Yanagisawa K, Kubota R, Yoshikawa Y, Ohue M, Akiyama Y.
    Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation.
    ACS Omega, 7(34): 30265-30274, 2022. doi: 10.1021/acsomega.2c03470
    Journal website | PubMed | GitHub
  54.  

  55. Kosugi T, Ohue M.
    Solubility-aware protein binding peptide design using AlphaFold.
    Biomedicines, 10(7): 1626, 2022. doi: 10.3390/biomedicines10071626
    Journal website | PubMed | bioRxiv | GitHub | 日本語の解説
  56.  

  57. Furui K, Ohue M.
    Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain.
    In Proceedings of The 19th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2022), 7 pages, 2022. doi: 10.1109/CIBCB55180.2022.9863032
    Proceedings website | * Download | arXiv | Presentation video
    * © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  58.  

  59. 大上雅史.
    AlphaFold2の登場と創薬への影響.
    革新的AI創薬~医療ビッグデータ、人工知能がもたらす創薬研究の未来像~, エヌ・ティー・エス, 164-175, 2022.
    Book website
  60.  

  61. Andreani J, Ohue M, Jiménez-García B.
    Web Tools for Modeling and Analysis of Biomolecular Interactions.
    Frontiers in Molecular Biosciences, 9:875859, 2022. doi: 10.3389/fmolb.2022.875859
    Journal website | PubMed
  62.  

  63. 大上雅史.
    AlphaFoldのタンパク質立体構造予測の性能.
    実験医学, 2022年2月号, 40(2): 427-430, 羊土社, 2022. doi: 10.18958/6977-00002-0000038-00
    Book website
  64.  

  65. 大上雅史.
    AlphaFold利用のすすめ.
    実験医学, 2022年2月号, 40(2): 433-438, 羊土社, 2022. doi: 10.18958/6977-00002-0000040-00
    Book website
  66.  

  67. Li J, Yanagisawa K, Yoshikawa Y, Ohue M, Akiyama Y.
    Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning.
    Bioinformatics, 38(4):1110-1117, 2022. doi: 10.1093/bioinformatics/btab726
    Journal website | PubMed | GitHub
  68.  

  69. Kosugi T, Ohue M.
    Quantitative estimate index for early-stage screening of compounds targeting protein-protein interactions.
    International Journal of Molecular Sciences, 22(20): 10925, 2021. doi: 10.3390/ijms222010925
    Journal website | PubMed | GitHub | 日本語の解説

     

  70. Takabatake K, Izawa K, Akikawa M, Yanagisawa K, Ohue M, Akiyama Y.
    Improved large-scale homology search by two-step seed search using multiple reduced amino acid alphabets.
    Genes, 12(9): 1455, 2021. doi: 10.3390/genes12091455
    Journal website | PubMed | GitHub
  71.  

  72. Sugita M, Sugiyama S, Fujie T, Yoshikawa Y, Yanagisawa K, Ohue M, Akiyama Y.
    Large-scale membrane permeability prediction of cyclic peptides crossing a lipid bilayer based on enhanced sampling molecular dynamics simulations.
    Journal of Chemical Information and Modeling, 61(7): 3681-3695, 2021. doi: 10.1021/acs.jcim.1c00380
    Journal website | PubMed
  73.  

  74. Izawa K, Okamoto-Shibayama K, Kita D, Tomita S, Saito A, Ishida T, Ohue M, Akiyama Y, Ishihara K.
    Taxonomic and gene category analyses of subgingival plaques from a group of Japanese individuals with and without periodontitis.
    International Journal of Molecular Sciences, 22(10): 5298, 2021. doi:10.3390/ijms22105298
    Journal website | PubMed
  75.  

  76. Ohue M, Aoyama K, Akiyama Y.
    High-performance cloud computing for exhaustive protein-protein docking.
    In Proceedings of The 26th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’20), Advances in Parallel & Distributed Processing and Applications, 737-746, 2021. doi:10.1007/978-3-030-69984-0_53
    Proceedings website | arXiv
  77.  

  78. Ohue M, Akiyama Y.
    MEGADOCK-GUI: a GUI-based complete cross-docking tool for exploring protein-protein interactions.
    In Proceedings of The 27th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’21), Advances in Parallel & Distributed Processing and Applications. (accepted)
    arXiv, Preprint, 2105.03617 [q-bio.BM], 2021.
    Proceedings website | arXiv
  79.  

  80. Ohue M, Watanabe H, Akiyama Y.
    MEGADOCK-Web-Mito: human mitochondrial protein-protein interaction prediction database.
    In Proceedings of The 27th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’21), Advances in Parallel & Distributed Processing and Applications. (accepted)
    arXiv, Preprint, 2105.00445 [q-bio.BM], 2021.
    Proceedings website | arXiv
  81.  

  82. Isawa K, Yanagisawa K, Ohue M, Akiyama Y.
    Antisense oligonucleotide activity analysis based on opening and binding energies to targets.
    In Proceedings of The 27th International Conference on Parallel & Distributed Processing Techniques and Applications (PDPTA’21), Advances in Parallel & Distributed Processing and Applications. (accepted)
    Proceedings website
  83.  

  84. Sugita S, Ohue M.
    Drug-target affinity prediction using applicability domain based on data density.
    In Proceedings of The 18th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2021), 6 pages, 2021. doi:10.1109/CIBCB49929.2021.9562808
    Proceedings website | * Download | ChemRxiv | Presentation video
    * © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  85.  

  86. Kosugi T, Ohue M.
    Quantitative estimate of protein-protein interaction targeting drug-likeness.
    In Proceedings of The 18th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2021), 8 pages, 2021. doi:10.1109/CIBCB49929.2021.9562931
    Proceedings website | * Download | ChemRxiv | Presentation video
    * © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  87.  

  88. Ohue M.
    Re-ranking of computational protein–peptide docking solutions with amino acid profiles of rigid-body docking results.
    In Proceedings of The 21st International Conference on Bioinformatics & Computational Biology (BIOCOMP’20), Advances in Computer Vision and Computational Biology, 749-758, 2021. doi:10.1007/978-3-030-71051-4_58
    Proceedings website | bioRxiv
  89.  

  90. Ito S, Senoo A, Nagatoishi S, Ohue M, Yamamoto M, Tsumoto K, Wakui N.
    Structural basis for binding mechanism of human serum albumin complexed with cyclic peptide dalbavancin.
    Journal of Medicinal Chemistry, 63(22): 14045–14053, 2020. doi:10.1021/acs.jmedchem.0c01578
    Journal website | bioRxiv | PubMed
  91.  

  92. Launay G†, Ohue M†*, Santero JP, Matsuzaki M, Hilpert C, Uchikoga N, Hayashi T, Martin J*.
    Evaluation of CONSRANK-like scoring functions for rescoring ensembles of protein-protein docking poses.
    Frontiers in Molecular Biosciences, 7:559005, 2020. doi:10.3389/fmolb.2020.559005
    Journal website | bioRxiv | PubMed
    † Co-first authors, * Co-corresponding authors
  93.  

  94. 大上雅史.
    構造情報に基づくタンパク質間相互作用の計算予測.
    ファインケミカル, 2020年10月号, 49(10): 25-31, シーエムシー出版, 2020.
    Book website
  95.  

  96. Aoyama K, Kakuta M, Matsuzaki Y, Ishida T, Ohue M, Akiyama Y.
    Development of computational pipeline software for genome/exome analysis on the K computer.
    Supercomputing Frontiers and Innovations, 7(1): 37-54, 2020. doi:10.14529/jsfi200102
    Journal website
  97.  

  98. Aoyama K, Watanabe H, Ohue M, Akiyama Y.
    Multiple HPC environments-aware container image configuration workflow for large-scale all-to-all protein-protein docking calculations.
    In Proceedings of the 6th Asian Conference on Supercomputing Frontiers (SCFA2020), Lecture Notes in Computer Science, 12082: 23-39, 2020. doi:10.1007/978-3-030-48842-0_2
    Proceedings website
  99.  

  100. Matsuno S, Ohue M, Akiyama Y.
    Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces.
    Biophysics and Physicobiology, 17: 2-13, 2020. doi:10.2142/biophysico.BSJ-2019050
    Journal website | PubMed
  101.  

  102. Chiba S, Ohue M, Gryniukova A, Borysko P, Zozulya S, Yasuo N, Yoshino R, Ikeda K, Shin WH, Kihara D, Iwadate M, Umeyama H, Ichikawa T, Teramoto R, Hsin KY, Gupta V, Kitano H, Sakamoto M, Higuchi A, Miura N, Yura K, Mochizuki M, Ramakrishnan C, Thangakani AM, Velmurugan D, Gromiha MM, Nakane I, Uchida N, Hakariya H, Tan M, Nakamura HK, Suzuki SD, Ito T, Kawatani M, Kudoh K, Takashina S, Yamamoto KZ, Moriwaki Y, Oda K, Kobayashi D, Okuno T, Minami S, Chikenji G, Prathipati P, Nagao C, Mohsen A, Ito M, Mizuguchi K, Honma T, Ishida T, Hirokawa T, Akiyama Y, Sekijima M.
    A prospective compound screening contest identified broader inhibitors for Sirtuin 1.
    Scientific Reports, 9: 19585, 2019. doi:10.1038/s41598-019-55069-y
    Journal website | PubMed
  103.  

  104. 大上雅史, 林孝紀, 秋山泰.
    タンパク質間相互作用と複合体構造の予測結果を検索できるウェブサイト「MEGADOCK-Web」.
    実験医学, 2019年6月号, 37(9): 1469-1474, 羊土社, 2019.
    Book website
  105.  

  106. Jiang K, Zhang D, Iino T, Kimura R, Nakajima T, Shimizu K, Ohue M, Akiyama Y.
    A playful tool for predicting protein-protein docking.
    In Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia (MUM 2019), Article No. 40, 5 pages, 2019. doi:10.1145/3365610.3368409
    Proceedings website | Download *
    * © ACM, 2019. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MUM2019.
  107.  

  108. 秋山泰, 大上雅史, 吉川寧, 和久井直樹.
    中分子創薬に適した特性を有する環状ペプチドのインシリコ設計.
    ペプチド創薬の最前線(木曽良明監修), 70-78, シーエムシー出版, 2019.
    Book website
  109.  

  110. Ohue M, Suzuki SD, Akiyama Y.
    Learning-to-rank technique based on ignoring meaningless ranking orders between compounds.
    Journal of Molecular Graphics and Modelling, 92: 192-200, 2019. doi:10.1016/j.jmgm.2019.07.009
    Journal website | PubMed | GitHub
  111.  

  112. Ban T, Ohue M, Akiyama Y.
    NRLMFβ: beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving performance of drug–target interaction prediction.
    Biochemistry and Biophysics Reports, 18: 100615, 2019. doi:10.1016/j.bbrep.2019.01.008
    Journal website | PubMed | GitHub
  113.  

  114. Mochizuki M, Suzuki SD, Yanagisawa K, Ohue M, Akiyama Y.
    QEX: Target-specific druglikeness filter enhances ligand-based virtual screening.
    Molecular Diversity, 23(1): 11–18, 2019. doi:10.1007/s11030-018-9842-3
    Journal website | PubMed
  115.  

  116. Yamamoto K, Yoshikawa Y, Ohue M, Inuki S, Ohno H, Oishi S.
    Synthesis of triazolo- and oxadiazolo-piperazines by gold(I)-catalyzed domino cyclization: application to the design of a mitogen activated protein (MAP) kinase inhibitor.
    Organic Letters, 21(2): 373-377, 2019. doi:10.1021/acs.orglett.8b03500
    Journal website | PubMed
  117.  

  118. Ohue M, Yamasawa M, Izawa K, Akiyama Y.
    Parallelized pipeline for whole genome shotgun metagenomics with GHOSTZ-GPU and MEGAN.
    In Proceedings of the 19th annual IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE 2019), 152-156, 2019. doi:10.1109/BIBE.2019.00035
    Download * | Proceedings website | slide
    * © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  119.  

  120. Ohue M, Ii R, Yanagisawa K, Akiyama Y.
    Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph.
    In Proceedings of the 2019 International Conference on Parallel and Distributed Processing Techniques & Applications (PDPTA’19), 122-128, 2019.
    Proceedings website | arXiv | slide
  121.  

  122. Aoyama K, Yamamoto Y, Ohue M, Akiyama Y.
    Performance evaluation of MEGADOCK protein-protein interaction prediction system implemented with distributed containers on a cloud computing environment.
    In Proceedings of the 2019 International Conference on Parallel and Distributed Processing Techniques & Applications (PDPTA’19), 175-181, 2019.
    Proceedings website
  123.  

  124. Tajimi T, Wakui N, Yanagisawa K, Yoshikawa Y, Ohue M, Akiyama Y.
    Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.
    BMC Bioinformatics, 19(Suppl 19): 527, 2018. doi:10.1186/s12859-018-2529-z
    Journal website | PubMed
  125.  

  126. Kami D, Kitani T, Nakamura A, Wakui N, Mizutani R, Ohue M, Kametani F, Akimitsu N, Gojo S.
    The DEAD-box RNA-binding protein DDX6 regulates parental RNA decay for cellular reprogramming to pluripotency.
    PLoS ONE, 13(10): e0203708, 2018. doi:10.1371/journal.pone.0203708
    Journal website | PubMed
  127.  

  128. Yanagisawa K, Komine S, Kubota R, Ohue M, Akiyama Y.
    Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm.
    Computational Biology and Chemistry, 74: 399-406, 2018. doi:10.1016/j.compbiolchem.2018.03.013
    Journal website | PubMed | Slide
  129.  

  130. Hayashi T, Matsuzaki Y, Yanagisawa K, Ohue M*, Akiyama Y*.
    MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.
    BMC Bioinformatics, 19(Suppl 4): 62, 2018. doi:10.1186/s12859-018-2073-x
    Journal website | PubMed | Slide
  131.  

  132. Ban T, Ohue M, Akiyama Y.
    Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem.
    Computational Biology and Chemistry, 73: 139-146, 2018. doi:10.1016/j.compbiolchem.2018.02.008
    Journal website | PubMed | Software
  133.  

  134. Suzuki SD, Ohue M, Akiyama Y.
    PKRank: A novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM.
    Artificial Life and Robotics, 23(2): 205-212, 2018. doi:10.1007/s10015-017-0416-8
    Journal website
  135.  

  136. Wakui N, Yoshino R, Yasuo N, Ohue M, Sekijima M.
    Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: a molecular dynamics simulation approach.
    Journal of Molecular Graphics and Modelling, 79: 166-174, 2018. doi:10.1016/j.jmgm.2017.11.011
    Journal website | PubMed
  137.  

  138. Yanagisawa K, Komine S, Suzuki SD, Ohue M, Ishida T, Akiyama Y.
    Spresso: An ultrafast compound pre-screening method based on compound decomposition.
    Bioinformatics, 33(23): 3836-3843, 2017. doi:10.1093/bioinformatics/btx178
    Journal website | PubMed | Software
  139.  

  140. Matsuzaki Y, Uchikoga N, Ohue M, Akiyama Y.
    Rigid-docking approaches to explore protein-protein interaction space.
    Advances in Biochemical Engineering/Biotechnology, 160: 33-55, 2017. doi:10.1007/10_2016_41
    Journal website | PubMed
  141.  

  142. Suzuki S, Ishida T, Ohue M, Kakuta M, Akiyama Y.
    GHOSTX: a fast sequence homology search tool for functional annotation of metagenomic data.
    Methods in Molecular Biology, 1611: 15-25, 2017. doi:10.1007/978-1-4939-7015-5_2
    Journal website | PubMed
  143.  

  144. Ohue M, Yamazaki T, Ban T, Akiyama Y.
    Link mining for kernel-based compound-protein interaction predictions using a chemogenomics approach.
    In the Thirteenth International Conference on Intelligent Computing (ICIC2017), Lecture Notes in Computer Science, 10362: 549-558, 2017. doi:10.1007/978-3-319-63312-1_48
    Proceedings website | arXiv | slide
  145.  

  146. Ban T, Ohue M, Akiyama Y.
    Efficient hyperparameter optimization by using Bayesian optimization for drug-target interaction prediction.
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  147. * © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

     

  148. Suzuki SD, Ohue M, Akiyama Y.
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    BMC Systems Biology, 9(Suppl 1): S6, 2015. doi:10.1186/1752-0509-9-S1-S6
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    FIT2013 第12回情報科学技術フォーラム講演論文集, 第2分冊(RG-001): 63-68, 2013.
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    * 本論文は一般社団法人 電子情報通信学会および一般社団法人 情報処理学会の著作権規程に基いて公開しているものです.
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