Shoichi Ishida

I mainly studied pharmacy as an undergraduate student, and I'm currently studying cheminformatics, bioinformatics, and computer science as a Ph.D. student.

I write blog posts related to my researches and life hacks on my web site.

Skills

• Pharmacist

Developments

• ReTReK: data-driven ReTrosynthesis planning application using Retrosynthesis Knowledge [GitHub]
• DEFMap: Dynamics Extraction From cryo-EM Map [GitHub]
• kGCN: A graph-based deep learning framework for life science [GitHub]
• CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration [GitHub]
• Reaction template extractor [GitHub]

Research Interests

• Computer-aided synthesis planning
• Application of machine learning for life sciences
• Cheminformatics
• Bioinformatics

Research Papers

Dagger (†) denotes these authors contributed equally.

• S. Matsumoto†, S. Ishida†, M. Araki, T. Kato, K. Terayama, Y. Okuno, Extraction of protein dynamics information from cryo-EM maps using deep learning, Nature Machine Intelligence, 2021
• S. Ishida, K. Terayama, R. Kojima, K. Takasu, Y. Okuno, Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks, Journal of Chemical Information and Modeling, 2019
• R. Kojima, S. Ishida, M. Ohta, H. Iwata, T. Honma, Y. Okuno, kGCN: a graph-based deep learning framework for chemical structures, Journal of Cheminformatics, 2020
• R. Shibukawa†, S. Ishida†, K. Yoshizoe, K. Wasa, K. Takasu, Y. Okuno, K. Terayama, K. Tsuda, CompRet: A comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration, Journal of Cheminformatics, 2020
• S. Ishida, K. Terayama, R. Kojima, K. Takasu, Y. Okuno, AI-Driven Synthetic Route Design with Retrosynthesis Knowledge, ChemRxiv, 2020

Conferences

• R. Kojima, H. Iwata, S. Ishida, M. Ikeguchi, Y. Okuno; A Prediction Framework for Drug Discovery Using Graph Convolutional Networks; 5th International Symposium for Medicinal Sciences (ISMS) (poster), March, 2019.

• S. Ishida, K. Terayama, R. Kojima, K. Takasu, Y. Okuno; Interpretable Reaction Prediction using Graph Convolutional Networks; CBI Annual Meeting, 2019/10/22~24, (oral)
• S. Ishida, K. Terayama, R. Kojima, K. Takasu, Y. Okuno; GCN 記述子とモンテカルロ木探索を用いた合成経路予測; CBI Annual Meeting, 2019/10/22~24, (oral: sponsored session)
• M. Ikeguchi, S. Ishida, R. Kojima, H. Iwata, Y. Okuno; Prediction of Compound-Protein Interactions and Visualization Based on Graph Convolutional Networks; CBI Annual Meeting, 2019/10/22~24, (poster)
• R. Shibukawa, S. Ishida, K. Terayama, K. Yoshizoe, Y. Okuno, K. Tsuda; 証明数探索を用いた化合物合成経路列挙アルゴリズム; Information-Based Induction Sciences (IBIS), 2018/11. (oral)

Review Articles

• 寺山慧, 石田祥一, 奥野恭史, 『AIによる逆合成解析に向けて』, 月刊「細胞」, Vol.51, No.7, pp.12-15, 2019/5.
• 寺山慧, 石田祥一, 奥野恭史, 『囲碁AIから逆合成解析へ−情報科学からのアプローチ』, 化学, Vol.74 No.2, pp.36-40, 2019/1.