Improvement of the Protein-Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: An Application to Interaction Pathway Analysis. - Masahito Ohue; Yuri Matsuzaki; Takashi Ishida 002; Yutaka Akiyama (PRIB, 2012)

Improvement of Protein-Protein Interaction Prediction by Integrating Template-Based and Template-Free Protein Docking. - Masahito Ohue; Yuri Matsuzaki; Takehiro Shimoda; Takashi Ishida 002; Yutaka Akiyama (BCB, 2013)

MEGADOCK-GPU: Acceleration of Protein-Protein Docking Calculation on GPUs. - Takehiro Shimoda; Takashi Ishida 002; Shuji Suzuki; Masahito Ohue; Yutaka Akiyama (BCB, 2013)

The MEGADOCK project: Ultra-high-speed protein-protein interaction prediction tools on supercomputing environments. - Takehiro Shimoda; Masahito Ohue; Yuri Matsuzaki; Takayuki Fujiwara; Nobuyuki Uchikoga; Takashi Ishida 002; Yutaka Akiyama (BCB, 2013)

MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments. - Yuri Matsuzaki; Nobuyuki Uchikoga; Masahito Ohue; Takehiro Shimoda; Toshiyuki Sato; Takashi Ishida 002; Yutaka Akiyama (Source Code Biol. Medicine, 2013)

MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers. - Masahito Ohue; Takehiro Shimoda; Shuji Suzuki; Yuri Matsuzaki; Takashi Ishida 002; Yutaka Akiyama (Bioinform., 2014)

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures. - Takehiro Shimoda; Shuji Suzuki; Masahito Ohue; Takashi Ishida 002; Yutaka Akiyama (BMC Syst. Biol., 2015)

Efficient hyperparameter optimization by using Bayesian optimization for drug-target interaction prediction. - Tomohiro Ban; Masahito Ohue; Yutaka Akiyama (ICCABS, 2017)

Link Mining for Kernel-Based Compound-Protein Interaction Predictions Using a Chemogenomics Approach. - Masahito Ohue; Takuro Yamazaki; Tomohiro Ban; Yutaka Akiyama (ICIC (2), 2017)

Spresso: an ultrafast compound pre-screening method based on compound decomposition. - Keisuke Yanagisawa; Shunta Komine; Shogo D. Suzuki; Masahito Ohue; Takashi Ishida 002; Yutaka Akiyama (Bioinform., 2017)

PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM. - Shogo D. Suzuki; Masahito Ohue; Yutaka Akiyama (Artif. Life Robotics, 2018)

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions. - Takanori Hayashi; Yuri Matsuzaki; Keisuke Yanagisawa; Masahito Ohue; Yutaka Akiyama (BMC Bioinform., 2018)

Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques. - Takashi Tajimi; Naoki Wakui; Keisuke Yanagisawa; Yasushi Yoshikawa; Masahito Ohue; Yutaka Akiyama (BMC Bioinform., 2018)

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem. - Tomohiro Ban; Masahito Ohue; Yutaka Akiyama (Comput. Biol. Chem., 2018)

Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm. - Keisuke Yanagisawa; Shunta Komine; Rikuto Kubota; Masahito Ohue; Yutaka Akiyama (Comput. Biol. Chem., 2018)

Parallelized Pipeline for Whole Genome Shotgun Metagenomics with GHOSTZ-GPU and MEGAN. - Masahito Ohue; Marina Yamasawa; Kazuki Izawa; Yutaka Akiyama (BIBE, 2019)

A playful tool for predicting protein-protein docking. - Keren Jiang; Di Zhang; Tsubasa Iino; Risa Kimura; Tatsuo Nakajima; Kana Shimizu; Masahito Ohue; Yutaka Akiyama (MUM, 2019)

Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph. - Masahito Ohue; Ryota Ii; Keisuke Yanagisawa; Yutaka Akiyama (CoRR, 2019)

Multiple HPC Environments-Aware Container Image Configuration Workflow for Large-Scale All-to-All Protein-Protein Docking Calculations. - Kento Aoyama; Hiroki Watanabe; Masahito Ohue; Yutaka Akiyama (SCFA, 2020)

High-performance cloud computing for exhaustive protein-protein docking. - Masahito Ohue; Kento Aoyama; Yutaka Akiyama (CoRR, 2020)

Development of Computational Pipeline Software for Genome/Exome Analysis on the K Computer. - Kento Aoyama; Masanori Kakuta; Yuri Matsuzaki; Takashi Ishida 002; Masahito Ohue; Yutaka Akiyama (Supercomput. Front. Innov., 2020)

Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness. - Takatsugi Kosugi; Masahito Ohue (CIBCB, 2021)

Drug-target affinity prediction using applicability domain based on data density. - Shunya Sugita; Masahito Ohue (CIBCB, 2021)

MEGADOCK-GUI: a GUI-based complete cross-docking tool for exploring protein-protein interactions. - Masahito Ohue; Yutaka Akiyama (CoRR, 2021)

Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations. - Masatake Sugita; Satoshi Sugiyama; Takuya Fujie; Yasushi Yoshikawa; Keisuke Yanagisawa; Masahito Ohue; Yutaka Akiyama (J. Chem. Inf. Model., 2021)

Compound Virtual Screening by Learning-to-Rank with Gradient Boosting Decision Tree and Enrichment-based Cumulative Gain. - Kairi Furui; Masahito Ohue (CIBCB, 2022)

Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning. - Jianan Li; Keisuke Yanagisawa; Yasushi Yoshikawa; Masahito Ohue; Yutaka Akiyama (Bioinform., 2022)

Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations. - Masatake Sugita; Takuya Fujie; Keisuke Yanagisawa; Masahito Ohue; Yutaka Akiyama (J. Chem. Inf. Model., 2022)

Enhancing Model Learning and Interpretation using Multiple Molecular Graph Representations for Compound Property and Activity Prediction. - Apakorn Kengkanna; Masahito Ohue (CIBCB, 2023)

Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation. - Kairi Furui; Masahito Ohue (CoRR, 2023)

CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides. - Jianan Li; Keisuke Yanagisawa; Masatake Sugita; Takuya Fujie; Masahito Ohue; Yutaka Akiyama (J. Chem. Inf. Model., 2023)

REALM: Region-Empowered Antibody Language Model for Antibody Property Prediction. - Toru Nishino; Noriji Kato; Takuya Tsutaoka; Yuanzhong Li; Masahito Ohue (BIBM, 2024)

Predicting Antibody Stability pH Values from Amino Acid Sequences: Leveraging Protein Language Models for Formulation Optimization. - Takuya Tsutaoka; Noriji Kato; Toru Nishino; Yuanzhong Li; Masahito Ohue (BIBM, 2024)

Improving Performance on Replica-Exchange Molecular Dynamics Simulations by Optimizing GPU Core Utilization. - Taisuke Boku; Masatake Sugita; Ryohei Kobayashi 001; Shinnosuke Furuya; Takuya Fujie; Masahito Ohue; Yutaka Akiyama (ICPP, 2024)

Active learning for energy-based antibody optimization and enhanced screening. - Kairi Furui; Masahito Ohue (CoRR, 2024)

Antibody complementarity-determining region design using AlphaFold2 and DDG predictor. - Takafumi Ueki; Masahito Ohue (J. Supercomput., 2024)

Fastlomap: faster lead optimization mapper algorithm for large-scale relative free energy perturbation. - Kairi Furui; Masahito Ohue (J. Supercomput., 2024)

Mathematical modeling and problem solving: from fundamentals to applications. - Masahito Ohue; Kotoyu Sasayama; Masami Takata (J. Supercomput., 2024)

PairMap: An Intermediate Insertion Approach for Improving the Accuracy of Relative Free Energy Perturbation Calculations for Distant Compound Transformations. - Kairi Furui; Takafumi Shimizu; Yutaka Akiyama; S. Roy Kimura; Yoh Terada; Masahito Ohue (J. Chem. Inf. Model., 2025)

Innovations in mathematical modeling, AI, and optimization techniques. - Masahito Ohue; Nobuaki Yasuo; Masami Takata (J. Supercomput., 2025)

NPGPT: natural product-like compound generation with GPT-based chemical language models. - Koh Sakano; Kairi Furui; Masahito Ohue (J. Supercomput., 2025)