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)

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)

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

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

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. - Tatsuya Yoshizawa; Shoichi Ishida; Tomohiro Sato; Masateru Ohta; Teruki Honma; Kei Terayama (J. Chem. Inf. Model., 2022)

AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data. - Yugo Shimizu; Masateru Ohta; Shoichi Ishida; Kei Terayama; Masanori Osawa; Teruki Honma; Kazuyoshi Ikeda (J. Cheminformatics, 2023)

Predicting condensate formation of protein and RNA under various environmental conditions. - Ka Yin Chin; Shoichi Ishida; Yukio Sasaki; Kei Terayama (BMC Bioinform., 2024)

Qcforever2: Advanced Automation of Quantum Chemistry Computations. - Masato Sumita; Kei Terayama; Shoichi Ishida; Kensuke Suga; Shohei Saito; Koji Tsuda (J. Comput. Chem., 2025)

Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices. - Hikari Tomita; Nobuhiro Nakamura; Shoichi Ishida; Toshio Kamiya; Kei Terayama (CoRR, 2025)

Large language models open new way of AI-assisted molecule design for chemists. - Shoichi Ishida; Tomohiro Sato; Teruki Honma; Kei Terayama (J. Cheminformatics, 2025)

GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy. - Hiroaki Shiokawa; Shoichi Ishida; Kei Terayama (J. Cheminformatics, 2025)

Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning. - Yusuke Kobayashi; Naoki Fujiwara; Yuki Murakami; Shoichi Ishida; Sho Kinguchi; Tatsuya Haze; Kengo Azushima; Akira Fujiwara; Hiromichi Wakui; Masayoshi Sakakura; Kei Terayama; Nobuhito Hirawa; et al. (BMC Medical Informatics Decis. Mak., 2025)