Biomedical Computational Intelligence Unit

Research summary

• Elucidation of structure, dynamics, and function of biomolecules by integrating of simulation, AI and experiments
• Development of network approach for elucidation of disease mechanisms and identification of target molecules for drug discovery
• Development of AI and simulation technologies for translational research between clinical and basic research

Main research results

Representative papers

• Matsumoto, S., Isaka, Y., Kanada, R., Ma, B., Araki, M., Chiba, S., Tokuhisa, A., Iwata, H., Ishida, S., Akinaga, Y. and Terayama, K., Kojima, R., Harada, Y., Kazuhiro, T., Teruki, H.,Kitao, A., and Okuno, Y. ,
  Precision spatiotemporal analysis of large-scale compound–protein interactions through molecular dynamics simulation.
  PNAS nexus, 4(3), p.pgaf094. 2025
• Tokuhisa, A., Akinaga, Y., Sasakura, Y., Terayama, K., Matsumoto, S., Kato, T. and Okuno, Y.,
  cryoMDM; Molecular simulation Driven structural Matching Approach to Estimate Variety of Atomic Three-dimensional Model Based on Noisy cryoEM Single Particle Images.
  bioRxiv, pp.2024-10. 2024
• Kanada, R., Tokuhisa, A., Nagasaka, Y., Okuno, S., Amemiya, K., Chiba, S., Bekker, G.J., Kamiya, N., Kato, K. and Okuno, Y.,
  Enhanced coarse-grained molecular dynamics simulation with a smoothed hybrid potential using a neural network model.
  Journal of Chemical Theory and Computation, 20(1), pp.7-17, 2023
• Oyama, Y., Tabuchi, A. and Tokuhisa, A.,
  Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku.
  In Proceedings of the 13th Workshop on AI and Scientific Computing at Scale using Flexible Computing (pp. 1-9)., 2023
• Kenichiro Takaba, Chiduru Watanabe, Atsushi Tokuhisa, Yoshinobu Akinaga, Biao Ma, Ryo Kanada, Mitsugu Araki, Yasushi Okuno, Yusuke Kawashima, Hirotomo Moriwaki, Norihito Kawashita, Teruki Honma, Kaori Fukuzawa, Shigenori Tanaka
  Protein–ligand binding affinity prediction of cyclin‐dependent kinase‐2 inhibitors by dynamically averaged fragment molecular orbital‐based interaction energy
  Journal of Computational Chemistry 43(20) 1362-1371 2022
• Nakamura K, Kojima R, Uchino E, Ono K, Yanagita M, Murashita K, Itoh K, Nakaji S, Okuno Y:
  “Health improvement framework for actionable treatment planning using a surrogate Bayesian model”
  Nature Communications. 12:3088, 2021.
• Matsumoto S, Ishida S, Araki M, Kato T, Terayama K, Okuno Y:
  "Extraction of protein dynamics information from cryo-EM maps using deep learning"
  Nature Machine Intelligence. 3:153-160, 2021.
• Tanaka, Y, Higashihara, K, Nakazawa, M.A, Yamashita F, Tamada Y, Okuno Y
  "Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection"
  Scientific Reports. 11:11241, 2021.
• 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, Teruki Honma
  "High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning. "
  Journal of chemical information and modeling 60(7) 3361-3368 2020
• Tokuhisa, A, Kanada, R, Chiba, S, Terayama K, Isaka Y, Ma B, Kamiya N, Okuno Y
  "Coarse-grained diffraction template matching model to retrieve multiconformational models for biomolecule structures from noisy diffraction patterns"
  Journal of Chemical Information and Modeling. 60(6): 2803-2818, 2020.

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