安田さんらとの研究が慶應義塾大学よりプレスリリースされました．
https://www.keio.ac.jp/ja/press-releases/2022/5/20/28-124022/

本研究では，分子動力学シミュレーションと機械学習手法を組み合わせることで，薬とタンパク質の結合親和性をタンパク質の構造ゆらぎから予測する新規手法を提案しました．
Nature社のCommunications Biologyに掲載されました．

<原論文情報>
Ikki Yasuda, Katsuhiro Endo, Eiji Yamamoto, Yoshinori Hirano, Kenji Yasuoka
“Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities”
Communications Biology (2022).
https://doi.org/10.1038/s42003-022-03416-7

Noriyoshi Arai, Eiji Yamamoto, Takahiro Koishi, Yoshinori Hirano, Kenji Yasuoka, and Toshikazu Ebisuzaki,
“Wetting hysteresis induces effective unidirectional water transport through a fluctuating nanochannel”,
arXiv:2204.09251.

Winarto, Eiji Yamamoto, and Kenji Yasuoka,
“Water molecules in CNT–Si₃N₄ membrane: Properties and the separation effect for water–alcohol solution”,
J. Chem. Phys. (2021)

Ikki Yasuda, Katsuhiro Endo, Eiji Yamamoto, Yoshinori Hirano, Kenji Yasuoka,
“Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach”,
arXiv:2109.01339

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