Shigeyuki Matsumoto

According to our database, Shigeyuki Matsumoto authored at least 11 papers between 2004 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Chemical genomics language model toward reliable and explainable compound-protein interaction exploration.

Takuto Koyama, Hayato Tsumura, Ryunosuke Okita, Kimihiro Yamazaki, Aki Hasegawa, Keiko Imamura, Takashi Kato, Hiroaki Iwata, Ryosuke Kojima, Haruhisa Inoue, Shigeyuki Matsumoto, Yasushi Okuno

J. Cheminformatics, December, 2026

Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains.

Takuto Koyama, Hiroaki Iwata, Ryosuke Kojima, Takao Otsuka, Aki Hasegawa, Seungeon Lee, Hiroshi Ueda, Toshiharu Morimoto, Ryoko Sasaki, Nao Torimoto, Sei Murakami, Manabu Tojo, Teruki Honma, Shigeyuki Matsumoto, Yasushi Okuno

J. Cheminformatics, December, 2026

Elucidating Ligand Charge Effects in MR1 Cell-Surface Translocation Using Molecular Simulations.

Toshiki Fujii, Mitsugu Araki, Shigeyuki Matsumoto, Biao Ma, Takao Otsuka, Gert-Jan Bekker, Narutoshi Kamiya, Hiroaki Ohno, Shinsuke Inuki, Yasushi Okuno

J. Chem. Inf. Model., 2026

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, Yasushi Okuno

J. Cheminformatics, December, 2025

HELM-BERT: A Transformer for Medium-sized Peptide Property Prediction.

Seungeon Lee, Takuto Koyama, Itsuki Maeda, Shigeyuki Matsumoto, Yasushi Okuno

CoRR, December, 2025

Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead Optimization.

Shoma Ito, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, Yuji Okamoto, Masataka Kuroda, Hitoshi Kawashima, Reiko Watanabe, Tomoki Yonezawa, Takaaki Sumiyoshi, Kazuyoshi Ikeda, Kenji Mizuguchi, Hiroaki Iwata, Yasushi Okuno

J. Chem. Inf. Model., 2025

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., December, 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., August, 2023

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

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

2004

A 3.9-μm pixel pitch VGA format 10-b digital output CMOS image sensor with 1.5 transistor/pixel.

Hidekazu Takahashi, Masakuni Kinoshita, Kazumichi Morita, Takahiro Shirai, Toshiaki Sato, Takayuki Kimura, Hiroshi Yuzurihara, Shunsuke Inoue, Shigeyuki Matsumoto

IEEE J. Solid State Circuits, 2004