吉留崇 (Takashi Yoshidome)

東北大学大学院工学研究科応用物理学専攻数理物理学分野准教授

吉留が参画した課題がCRESTに採択され、2022年10月よりスタートしました。
「次世代放射光X線ナノCT計測の確立と展開」（研究代表者：高山裕貴）
T. Yoshidome,
"Four-Dimensional Imaging for Cryo-Electron Microscopy Experiments Using Molecular Simulations and Manifold Learning",
Journal of Computational Chemistry, Vol. 45, 738 (2024). [Open access]
T. Yoshidome,
"Mechanism of a High Free-energy Transduction Efficiency of Bacillus PS3 F1-ATPase from the Perspective of Solvent Entropy",
Journal of Molecular Liquids, Vol. 391, 123346 (2023).
K. Kawama, Y. Fukushima, M. Ikeguchi, M. Ohta, and T. Yoshidome (Corresponding author, 学生指導)
"gr Predictor: A Deep-Learning Model For Predicting the Hydration Structure of Proteins",
Journal of Chemical Information and Modeling, Vol. 62, 4460 (2022).
プログラム"gr Predictor"：https://github.com/YoshidomeGroup-Hydration/gr-predictor
小田垣孝、吉留崇、大久保毅、
「現代の物性物理学」(M. L. Cohen and S. G. Louie: Fundamentals of Condensed Matter Physics)
吉岡書店、2021年9月出版 Amazonのページ

Takashi Yoshidome,
"Hybrid of Manifold Learning and Molecular Simulation to Reconstruct the Protein Conformational Change using Cryo-Electron Microscopy Experiment"（Poster, 6/26）
Taichi Ito and Takashi Yoshidome,
“Fast Computational Method for the Hydration Free Energy” (Poster, 6/26)
Souta Kadowaki and Takashi Yoshidome,
“Molecular Dynamics Study of the Unfolding Processes of Proteins with Highly Similar Native Structure” (Poster, 6/27)
Yuki Ito, Masateru Ohta, Mitsunori Ikeguchi, and Takashi Yoshidome
“Deep Learning-Based Water Molecule Displacement Prediction Method for Improving the Accuracy of Drug Discovery Docking Software” (Poster, 6/28)

Shota Arai, Gota Kikugawa, Yuki Takayama, and Takashi Yoshidome,
"Manifold-Learning Approach to Material Data: Application to Experimental Data of Liquid Substances and Porous Structural Data"（Poster）

Takashi Yoshidome, Kosuke Kawama, Yusaku Fukushima, Mitsunori Ikeguchi, Masateru Ohta,
"Deep-Learning Model For Fast And Accurate Computation Of Hydration Structures Around Proteins"（Invited）