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Research
HPC- and AI-driven Drug Development Platform Division
Molecular Design Computational Intelligence Unit
Molecular Design Computational Intelligence Unit
Japanese
Unit Leader Mitsunori Ikeguchi
mitsunori.ikeguchi[at]riken.jp (Lab location: Yokohama)
- Please change [at] to @
- 2021
- Unit Leader, Molecular Design Computational Intelligence Unit, HPC and AI driven Drug Development Platform Division, R-CCS (-present)
- 2017
- Unit Leader, Molecular Design Data Intelligence Unit, Drug Development Data Intelligence Platform Group, MIH, RIKEN
- 2015
- Professor, Graduate School of Medical Life Science, Yokohama City University (-present)
- 2007
- Associate Professor, Graduate School of Medical Life Science, Yokohama City University
- 2001
- Assistant Professor, Graduate School of Medical Life Science, Yokohama City University
- 1996
- Research Associate, Graduate School of Agricultural and Life Sciences, The University of Tokyo
- 1994
- Ph.D. in Agriculture, Graduate School of Agricultural and Life Sciences, The University of Tokyo
Keyword
- Molecular simulation
- Artificial Intelligence
- Machine learning
- In-slilco drug design
Research summary
The aim of Molecular Design Computational Intelligence Unit is to develop novel hybrid computational methods of molecular simulation and artificial intelligence for rational drug design. Combining artificial intelligence with molecular simulation will extend the applicability of in-silico drug design in terms of both accuracy and efficiency. In addition to the rational design of conventional small molecules, middle-sized and macro molecules are also the targets of newly developed computational methods.
Representative papers
- Tsutomu Yamane, Toru Ekimoto, Mitsunori Ikeguchi:
Development of the force field for cyclosporine A.
Biophys. Physicobiol. 19:e190045 (2022). - Kosuke Kawama, Yusaku Fukushima, Mitsunori Ikeguchi, Masateru Ohta, Takashi Yoshidome:
gr Predictor: A Deep Learning Model for Predicting the Hydration Structures around Proteins.
J. Chem. Inf. Model. 62(18):4460-4473 (2022). - Kazu Osaki, Toru Ekimoto, Tsutomu Yamane, Mitsunori Ikeguchi:
3D-RISM-AI: A Machine Learning Approach to Predict Protein-Ligand Binding Affinity Using 3D-RISM.
J. Phys. Chem. B. 126(33):6148-6158 (2022). - Ikuko Miyaguchi, Miwa Sato, Akiko Kashima, Hiroyuki Nakagawa, Yuichi Kokabu, Biao Ma, Shigeyuki Matsumoto, Atsushi Tokuhisa, Masateru Ohta, Mitsunori Ikeguchi:
Machine learning to estimate the local quality of protein crystal structures.
Sci Rep. 11(1):23599 (2021). - Takashi Yoshidome, Mitsunori Ikeguchi, Masateru Ohta:
Comprehensive 3D‐RISM analysis of the hydration of small molecule binding sites in ligand‐free protein structures.
J. Comput. Chem. 41(28): 2406-2419 (2020). - Koichiro Kato, Tomohide Masuda, Chiduru Watanabe, Naoki Miyagawa, Hideo Mizouchi, Shumpei Nagase, Kikuko Kamisaka, Kanji Oshima, Satoshi Ono, Hiroshi Ueda, Atsushi Tokuhisa, Ryo Kanada, Masateru Ohta, Mitsunori Ikeguchi, Yasushi Okuno, Kaori Fukuzawa, and Teruki Honma:
High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning.
J Chem Inf Model. 60 (7): 3361-3368 (2020). - Shuntaro Chiba, Aki Tanabe, Makoto Nakakido, Yasushi Okuno, Kouhei Tsumoto, Masateru Ohta:
Structure-based design and discovery of novel anti-tissue factor antibodies with cooperative double-point mutations, using interaction analysis.
Sci Rep. 10(1):17590 (2020). - Satomi Kori, Tomohiro Jimenji, Toru Ekimoto, Miwa Sato, Fumie Kusano, Takashi Oda, Motoko Unoki, Mitsunori Ikeguchi, Kyohei Arita:
Serine 298 Phosphorylation in Linker 2 of UHRF1 Regulates Ligand-Binding Property of Its Tandem Tudor Domain.
J. Mol. Biol. 432, 4061-4075 (2020). - Shuntaro Chiba, Yasushi Okuno, Teruki Honma, Mitsunori Ikeguchi:
Force-field parametrization based on radial and energy distribution functions.
J. Comput. Chem. 40 (29): 2577-2585 (2019). - Toru Ekimoto, Tsutomu Yamane, Mitsunori Ikeguchi:
Elimination of finite-size effects on binding free energies via the warp-drive method.
J. Chem. Theory Comput. 14, 6544-6559 (2018).