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ORCIDMasahito Ohue
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2020 – today
- 2024
[j17]Takafumi Ueki, Masahito Ohue:
Antibody complementarity-determining region design using AlphaFold2 and DDG predictor. J. Supercomput. 80(9): 11989-12002 (2024)- [j16]Masahito Ohue, Kotoyu Sasayama, Masami Takata:
Mathematical modeling and problem solving: from fundamentals to applications. J. Supercomput. 80(10): 14116-14119 (2024) - [j15]Kairi Furui
, Masahito Ohue:
Fastlomap: faster lead optimization mapper algorithm for large-scale relative free energy perturbation. J. Supercomput. 80(10): 14417-14432 (2024) - [c14]Taisuke Boku, Masatake Sugita, Ryohei Kobayashi, Shinnosuke Furuya, Takuya Fujie, Masahito Ohue, Yutaka Akiyama:
Improving Performance on Replica-Exchange Molecular Dynamics Simulations by Optimizing GPU Core Utilization. ICPP 2024: 1082-1091 - 2023
- [j14]Jianan Li, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Masahito Ohue, Yutaka Akiyama:
CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides. J. Chem. Inf. Model. 63(7): 2240-2250 (2023) - [c13]Apakorn Kengkanna, Masahito Ohue:
Enhancing Model Learning and Interpretation using Multiple Molecular Graph Representations for Compound Property and Activity Prediction. CIBCB 2023: 1-8 - [i7]Kairi Furui, Masahito Ohue:
Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation. CoRR abs/2304.04713 (2023) - [i6]Apakorn Kengkanna, Masahito Ohue:
Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction. CoRR abs/2304.06253 (2023) - 2022
- [j13]Jianan Li, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama:
Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning. Bioinform. 38(4): 1110-1117 (2022) - [j12]Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama:
Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations. J. Chem. Inf. Model. 62(18): 4549-4560 (2022) - [c12]Kairi Furui, Masahito Ohue:
Compound Virtual Screening by Learning-to-Rank with Gradient Boosting Decision Tree and Enrichment-based Cumulative Gain. CIBCB 2022: 1-7 - [i5]Kairi Furui, Masahito Ohue:
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain. CoRR abs/2205.02169 (2022) - 2021
- [j11]Masatake Sugita, Satoshi Sugiyama, Takuya Fujie, Yasushi Yoshikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama:
Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations. J. Chem. Inf. Model. 61(7): 3681-3695 (2021) - [c11]Takatsugi Kosugi, Masahito Ohue:
Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness. CIBCB 2021: 1-8 - [c10]Shunya Sugita, Masahito Ohue:
Drug-target affinity prediction using applicability domain based on data density. CIBCB 2021: 1-6 - [i4]Masahito Ohue, Yutaka Akiyama:
MEGADOCK-GUI: a GUI-based complete cross-docking tool for exploring protein-protein interactions. CoRR abs/2105.03617 (2021) - 2020
- [j10]Kento Aoyama, Masanori Kakuta, Yuri Matsuzaki, Takashi Ishida, Masahito Ohue, Yutaka Akiyama:
Development of Computational Pipeline Software for Genome/Exome Analysis on the K Computer. Supercomput. Front. Innov. 7(1): 37-54 (2020) - [c9]Kento Aoyama, Hiroki Watanabe, Masahito Ohue, Yutaka Akiyama:
Multiple HPC Environments-Aware Container Image Configuration Workflow for Large-Scale All-to-All Protein-Protein Docking Calculations. SCFA 2020: 23-39 - [i3]Masahito Ohue, Kento Aoyama, Yutaka Akiyama:
High-performance cloud computing for exhaustive protein-protein docking. CoRR abs/2006.08905 (2020)
2010 – 2019
- 2019
- [c8]Masahito Ohue, Marina Yamasawa, Kazuki Izawa, Yutaka Akiyama:
Parallelized Pipeline for Whole Genome Shotgun Metagenomics with GHOSTZ-GPU and MEGAN. BIBE 2019: 152-156 - [c7]Keren Jiang, Di Zhang, Tsubasa Iino, Risa Kimura, Tatsuo Nakajima, Kana Shimizu, Masahito Ohue, Yutaka Akiyama:
A playful tool for predicting protein-protein docking. MUM 2019: 40:1-40:5 - [i2]Masahito Ohue, Ryota Ii, Keisuke Yanagisawa, Yutaka Akiyama:
Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph. CoRR abs/1907.01103 (2019) - 2018
- [j9]Shogo D. Suzuki, Masahito Ohue, Yutaka Akiyama:
PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM. Artif. Life Robotics 23(2): 205-212 (2018) - [j8]Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama:
MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions. BMC Bioinform. 19-S(4): 61-72 (2018) - [j7]Takashi Tajimi, Naoki Wakui, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama:
Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques. BMC Bioinform. 19-S(19): 157-170 (2018) - [j6]Tomohiro Ban, Masahito Ohue, Yutaka Akiyama:
Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem. Comput. Biol. Chem. 73: 139-146 (2018) - [j5]Keisuke Yanagisawa, Shunta Komine, Rikuto Kubota, Masahito Ohue, Yutaka Akiyama:
Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm. Comput. Biol. Chem. 74: 399-406 (2018) - 2017
- [j4]Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama:
Spresso: an ultrafast compound pre-screening method based on compound decomposition. Bioinform. 33(23): 3836-3843 (2017) - [c6]Tomohiro Ban, Masahito Ohue, Yutaka Akiyama:
Efficient hyperparameter optimization by using Bayesian optimization for drug-target interaction prediction. ICCABS 2017: 1-6 - [c5]Masahito Ohue, Takuro Yamazaki, Tomohiro Ban, Yutaka Akiyama:
Link Mining for Kernel-Based Compound-Protein Interaction Predictions Using a Chemogenomics Approach. ICIC (2) 2017: 549-558 - [i1]Masahito Ohue, Takuro Yamazaki, Tomohiro Ban, Yutaka Akiyama:
Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach. CoRR abs/1705.01667 (2017) - 2015
- [j3]Takehiro Shimoda, Shuji Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama:
Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures. BMC Syst. Biol. 9(S-1): S6 (2015) - 2014
- [j2]Masahito Ohue, Takehiro Shimoda, Shuji Suzuki, Yuri Matsuzaki, Takashi Ishida, Yutaka Akiyama:
MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers. Bioinform. 30(22): 3281-3283 (2014) - 2013
- [j1]Yuri Matsuzaki, Nobuyuki Uchikoga, Masahito Ohue, Takehiro Shimoda, Toshiyuki Sato, Takashi Ishida, Yutaka Akiyama:
MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments. Source Code Biol. Medicine 8: 18 (2013) - [c4]Masahito Ohue, Yuri Matsuzaki, Takehiro Shimoda, Takashi Ishida, Yutaka Akiyama:
Improvement of Protein-Protein Interaction Prediction by Integrating Template-Based and Template-Free Protein Docking. BCB 2013: 666 - [c3]Takehiro Shimoda, Masahito Ohue, Yuri Matsuzaki, Takayuki Fujiwara, Nobuyuki Uchikoga, Takashi Ishida, Yutaka Akiyama:
The MEGADOCK project: Ultra-high-speed protein-protein interaction prediction tools on supercomputing environments. BCB 2013: 667 - [c2]Takehiro Shimoda, Takashi Ishida, Shuji Suzuki, Masahito Ohue, Yutaka Akiyama:
MEGADOCK-GPU: Acceleration of Protein-Protein Docking Calculation on GPUs. BCB 2013: 883 - 2012
- [c1]Masahito Ohue, Yuri Matsuzaki, Takashi Ishida, Yutaka Akiyama:
Improvement of the Protein-Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: An Application to Interaction Pathway Analysis. PRIB 2012: 178-187
Coauthor Index
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