LIGAND: database of chemical compounds and reactions in biological pathways. - Susumu Goto; Yasushi Okuno; Masahiro Hattori; Takaaki Nishioka; Minoru Kanehisa (Nucleic Acids Res., 2002)

Mining biologically active patterns in metabolic pathways using microarray expression profiles. - Hiroshi Mamitsuka; Yasushi Okuno; Atsuko Yamaguchi (SIGKDD Explor., 2003)

A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths Using Gene Expression and Protein Classes. - Hiroshi Mamitsuka; Yasushi Okuno (CSB, 2004)

The KEGG resource for deciphering the genome. - Minoru Kanehisa; Susumu Goto; Shuichi Kawashima; Yasushi Okuno; Masahiro Hattori (Nucleic Acids Res., 2004)

A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. - Shanfeng Zhu; Yasushi Okuno; Gozoh Tsujimoto; Hiroshi Mamitsuka (ECCB/JBI, 2005)

GLIDA: GPCR-ligand database for chemical genomic drug discovery. - Yasushi Okuno; Jiyoon Yang; Kei Taneishi; Hiroaki Yabuuchi; Gozoh Tsujimoto (Nucleic Acids Res., 2006)

Predicting implicit associated cancer genes from OMIM and MEDLINE by a new probabilistic model. - Shanfeng Zhu; Yasushi Okuno; Gozoh Tsujimoto; Hiroshi Mamitsuka (BMC Syst. Biol., 2007)

GLIDA: GPCR - ligand database for chemical genomics drug discovery - database and tools update. - Yasushi Okuno; Akiko Tamon; Hiroaki Yabuuchi; Satoshi Niijima; Yohsuke Minowa; Koichiro Tonomura; Ryo Kunimoto; Chunlai Feng (Nucleic Acids Res., 2008)

Substructure Mining of GPCR Ligands Reveals Activity-Class Specific Functional Groups in an Unbiased Manner. - Eelke van der Horst; Yasushi Okuno; Andreas Bender 002; Adriaan P. IJzerman (J. Chem. Inf. Model., 2009)

Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection. - Satoshi Niijima; Yasushi Okuno (IEEE ACM Trans. Comput. Biol. Bioinform., 2009)

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization. - Eelke van der Horst; Julio E. Peironcely; Adriaan P. IJzerman; Margot W. Beukers; Jonathan Robert Lane; Herman W. T. van Vlijmen; Michael T. M. Emmerich; Yasushi Okuno; Andreas Bender 002 (BMC Bioinform., 2010)

Cross-Target View to Feature Selection: Identification of Molecular Interaction Features in Ligand-Target Space. - Satoshi Niijima; Hiroaki Yabuuchi; Yasushi Okuno (J. Chem. Inf. Model., 2011)

Chemogenomic approach to comprehensive predictions of ligand-target interactions: A comparative study. - J. B. Brown; Satoshi Niijima; Akira Shiraishi; Masahiko Nakatsui; Yasushi Okuno (BIBM Workshops, 2012)

Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors. - Satoshi Niijima; Akira Shiraishi; Yasushi Okuno (J. Chem. Inf. Model., 2012)

On the Behavior of Tile Assembly System at High Temperatures. - Shinnosuke Seki 001; Yasushi Okuno (Comput., 2013)

Chemical Genomics Approach for GPCR-Ligand Interaction Prediction and Extraction of Ligand Binding Determinants. - Akira Shiraishi; Satoshi Niijima; J. B. Brown; Masahiko Nakatsui; Yasushi Okuno (J. Chem. Inf. Model., 2013)

On computational complexity of graph inference from counting. - Szilárd Zsolt Fazekas; Hiro Ito; Yasushi Okuno; Shinnosuke Seki 001; Kei Taneishi (Nat. Comput., 2013)

Computational chemogenomics: Is it more than inductive transfer? - J. B. Brown; Yasushi Okuno; Gilles Marcou; Alexandre Varnek; Dragos Horvath (J. Comput. Aided Mol. Des., 2014)

The Effect of Conformational Flexibility on Binding Free Energy Estimation between Kinases and Their Inhibitors. - Mitsugu Araki; Narutoshi Kamiya; Miwa Sato; Masahiko Nakatsui; Takatsugu Hirokawa; Yasushi Okuno (J. Chem. Inf. Model., 2016)

Construction of Knowledge-Base for Clinical Interpretation of Genomic Variants. - Mayumi Kamada; Toshiaki Katayama; Shuichi Kawashima; Ryosuke Kojima; Masahiko Nakatsui; Yasushi Okuno (SWAT4LS, 2017)

Machine learning accelerates MD-based binding pose prediction between ligands and proteins. - Kei Terayama; Hiroaki Iwata; Mitsugu Araki; Yasushi Okuno; Koji Tsuda (Bioinform., 2018)

Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach. - Mitsugu Araki; Hiroaki Iwata; Biao Ma; Atsuto Fujita; Kei Terayama; Yukari Sagae; Fumie Ono; Koji Tsuda; Narutoshi Kamiya; Yasushi Okuno (J. Comput. Chem., 2018)

Med2RDF: Semantic Biomedical Knowledge-base and APIs for the Clinical Genome Medicine. - Mayumi Kamada; Toshiaki Katayama; Shuichi Kawashima; Ryosuke Kojima; Masahiko Nakatsui; Yasushi Okuno (SWAT4HCLS, 2019)

Force-field parametrization based on radial and energy distribution functions. - Shuntaro Chiba; Yasushi Okuno; Teruki Honma; Mitsunori Ikeguchi (J. Comput. Chem., 2019)

Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks. - Shoichi Ishida; Kei Terayama; Ryosuke Kojima; Kiyosei Takasu; Yasushi Okuno (J. Chem. Inf. Model., 2019)

Health improvement framework for planning actionable treatment process using surrogate Bayesian model. - Kazuki Nakamura; Ryosuke Kojima; Eiichiro Uchino; Koichi Murashita; Ken Itoh; Shigeyuki Nakaji; Yasushi Okuno (CoRR, 2020)

Prediction of blood pressure variability using deep neural networks. - Hiroshi Koshimizu; Ryosuke Kojima; Kazuomi Kario; Yasushi Okuno (Int. J. Medical Informatics, 2020)

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach. - Eiichiro Uchino; Kanata Suzuki; Noriaki Sato; Ryosuke Kojima; Yoshinori Tamada; Shusuke Hiragi; Hideki Yokoi; Nobuhiro Yugami; Sachiko Minamiguchi; Hironori Haga; Motoko Yanagita; Yasushi Okuno (Int. J. Medical Informatics, 2020)

Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking. - Gert-Jan Bekker; Mitsugu Araki; Kanji Oshima; Yasushi Okuno; Narutoshi Kamiya (J. Comput. Chem., 2020)

kGCN: a graph-based deep learning framework for chemical structures. - Ryosuke Kojima; Shoichi Ishida; Masateru Ohta; Hiroaki Iwata; Teruki Honma; Yasushi Okuno (J. Cheminformatics, 2020)

CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration. - Ryosuke Shibukawa; Shoichi Ishida; Kazuki Yoshizoe; Kunihiro Wasa; Kiyosei Takasu; Yasushi Okuno; Kei Terayama; Koji Tsuda (J. Cheminformatics, 2020)

High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning. - Koichiro Kato; Tomohide Masuda; Chiduru Watanabe; Naoki Miyagawa; Hideo Mizouchi; Shumpei Nagase; Kikuko Kamisaka; Kanji Oshima; Satoshi Ono; Hiroshi Ueda; Atsushi Tokuhisa; Ryo Kanada; et al. (J. Chem. Inf. Model., 2020)

Coarse-Grained Diffraction Template Matching Model to Retrieve Multiconformational Models for Biomolecule Structures from Noisy Diffraction Patterns. - Atsushi Tokuhisa; Ryo Kanada; Shuntaro Chiba; Kei Terayama; Yuta Isaka; Biao Ma; Narutoshi Kamiya; Yasushi Okuno (J. Chem. Inf. Model., 2020)

Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data. - Noriaki Sato; Eiichiro Uchino; Ryosuke Kojima; Shusuke Hiragi; Motoko Yanagita; Yasushi Okuno (Comput. Methods Programs Biomed., 2021)

Complex network prediction using deep learning. - Yoshihisa Tanaka; Ryosuke Kojima; Shoichi Ishida; Fumiyoshi Yamashita; Yasushi Okuno (CoRR, 2021)

Accurate Binding Configuration Prediction of a G-Protein-Coupled Receptor to Its Antagonist Using Multicanonical Molecular Dynamics-Based Dynamic Docking. - Gert-Jan Bekker; Mitsugu Araki; Kanji Oshima; Yasushi Okuno; Narutoshi Kamiya (J. Chem. Inf. Model., 2021)

Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. - Biao Ma; Kei Terayama; Shigeyuki Matsumoto; Yuta Isaka; Yoko Sasakura; Hiroaki Iwata; Mitsugu Araki; Yasushi Okuno (J. Chem. Inf. Model., 2021)

eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping. - Shuntaro Chiba; Kenji Rowel Q. Lim; Narin Sheri; Saeed Anwar; Esra Erkut; Md Nur Ahad Shah; Tejal Aslesh; Stanley Woo; Omar Sheikh; Rika Maruyama; Hiroaki Takano; Katsuhiko Kunitake; et al. (Nucleic Acids Res., 2021)

Extraction of protein dynamics information from cryo-EM maps using deep learning. - Shigeyuki Matsumoto; Shoichi Ishida; Mitsugu Araki; Takayuki Kato; Kei Terayama; Yasushi Okuno (Nat. Mach. Intell., 2021)

Differential Privacy under Incalculable Sensitivity. - Tomoaki Mimoto; Masayuki Hashimoto; Hiroyuki Yokoyama; Toru Nakamura; Takamasa Isohara; Ryosuke Kojima; Aki Hasegawa; Yasushi Okuno (CSP, 2022)

CBNplot: Bayesian network plots for enrichment analysis. - Noriaki Sato; Yoshinori Tamada; Guangchuang Yu; Yasushi Okuno (Bioinform., 2022)

GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network. - Atsuko Takagi; Mayumi Kamada; Eri Hamatani; Ryosuke Kojima; Yasushi Okuno (CoRR, 2022)

Using incomplete Cholesky factorization to increase the time step in molecular dynamics simulations. - Takumi Washio; Xiaoke Cui; Ryo Kanada; Jun-ichi Okada; Seiryo Sugiura; Yasushi Okuno; Shoji Takada; Toshiaki Hisada (J. Comput. Appl. Math., 2022)

Protein-ligand binding affinity prediction of cyclin-dependent kinase-2 inhibitors by dynamically averaged fragment molecular orbital-based interaction energy. - Kenichiro Takaba; Chiduru Watanabe; Atsushi Tokuhisa; Yoshinobu Akinaga; Biao Ma; Ryo Kanada; Mitsugu Araki; Yasushi Okuno; Yusuke Kawashima; Hirotomo Moriwaki; Norihito Kawashita; Teruki Honma; et al. (J. Comput. Chem., 2022)

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge. - Shoichi Ishida; Kei Terayama; Ryosuke Kojima; Kiyosei Takasu; Yasushi Okuno (J. Chem. Inf. Model., 2022)

Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network. - Atsushi Tokuhisa; Yoshinobu Akinaga; Kei Terayama; Yuji Okamoto; Yasushi Okuno (J. Chem. Inf. Model., 2022)

An Auto-Encoder to Reconstruct Structure with Cryo-EM Images via Theoretically Guaranteed Isometric Latent Space, and Its Application for Automatically Computing the Conformational Pathway. - Kimihiro Yamazaki; Yuichiro Wada; Atsushi Tokuhisa; Mutsuyo Wada; Takashi Katoh; Yuhei Umeda; Yasushi Okuno; Akira Nakagawa (MICCAI (1), 2023)

An end-to-end framework for gene expression classification by integrating a background knowledge graph: application to cancer prognosis prediction. - Kazuma Inoue; Ryosuke Kojima; Mayumi Kamada; Yasushi Okuno (CoRR, 2023)

A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data. - Aya Nakamura; Ryosuke Kojima; Yuji Okamoto; Eiichiro Uchino; Yohei Mineharu; Yohei Harada; Mayumi Kamada; Manabu Muto; Motoko Yanagita; Yasushi Okuno (CoRR, 2023)

Privacy-Preserving Correlation Coefficient. - Tomoaki Mimoto; Hiroyuki Yokoyama; Toru Nakamura; Takamasa Isohara; Masayuki Hashimoto; Ryosuke Kojima; Aki Hasegawa; Yasushi Okuno (IEICE Trans. Inf. Syst., 2023)

Individual health-disease phase diagrams for disease prevention based on machine learning. - Kazuki Nakamura; Eiichiro Uchino; Noriaki Sato; Ayano Araki; Kei Terayama; Ryosuke Kojima; Koichi Murashita; Ken Itoh; Tatsuya Mikami; Yoshinori Tamada; Yasushi Okuno (J. Biomed. Informatics, 2023)

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples. - Takuto Koyama; Shigeyuki Matsumoto; Hiroaki Iwata; Ryosuke Kojima; Yasushi Okuno (J. Chem. Inf. Model., 2023)

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network. - Narumi Hatano; Mayumi Kamada; Ryosuke Kojima; Yasushi Okuno (BMC Bioinform., 2023)

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search. - Hiroaki Iwata; Taichi Nakai; Takuto Koyama; Shigeyuki Matsumoto; Ryosuke Kojima; Yasushi Okuno (J. Chem. Inf. Model., 2023)

Subgrouping Causal Networks of Disease Onset in Large-scale Health and Medical Data using Supercomputer Fugaku. - Taisei Tosaki; Eiichiro Uchino; Yohei Harada; Minoru Sakuragi; Yusuke Koyanagi; Seiji Okajima; Hirofumi Suzuki; Kentaro Kanamori; Masahiro Asaoka; Kouji Kurihara; Takuya Takagi; Koji Maruhashi; et al. (BIBM, 2024)

Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction. - Taisei Tosaki; Eiichiro Uchino; Ryosuke Kojima; Yohei Mineharu; Mikio Arita; Nobuyuki Miyai; Yoshinori Tamada; Tatsuya Mikami; Koichi Murashita; Shigeyuki Nakaji; Yasushi Okuno (CoRR, 2024)

Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking. - Gert-Jan Bekker; Kanji Oshima; Mitsugu Araki; Yasushi Okuno; Narutoshi Kamiya (J. Chem. Inf. Model., 2024)

Supervised Graph Contrastive Learning for Gene Regulatory Network. - Sho Oshima; Yuji Okamoto; Taisei Tosaki; Ryosuke Kojima; Yasushi Okuno (CoRR, 2025)

kMoL: an open-source machine and federated learning library for drug discovery. - Romeo Cozac; Haris Hasic; Jun Jin Choong; Vincent Richard; Loic Beheshti; Cyrille Froehlich; Takuto Koyama; Shigeyuki Matsumoto; Ryosuke Kojima; Hiroaki Iwata; Aki Hasegawa; Takao Otsuka; et al. (J. Cheminformatics, 2025)