Zobrazeno 1 - 9 of 9 pro vyhledávání: '"Yasuda, Ikki"'
1
Report
Prediction of transport property via machine learning molecular movements
Autor: Yasuda, Ikki, Kobayashi, Yusei, Endo, Katsuhiro, Hayakawa, Yoshihiro, Fujiwara, Kazuhiko, Yajima, Kuniaki, Arai, Noriyoshi, Yasuoka, Kenji
Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodynamic properties, and a
Externí odkaz: http://arxiv.org/abs/2203.03103
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2
Report
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach
Autor: Yasuda, Ikki, Endo, Katsuhiro, Yamamoto, Eiji, Hirano, Yoshinori, Yasuoka, Kenji
Publikováno v: Commun. Biol. 5, 481 (2022)
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dy
Externí odkaz: http://arxiv.org/abs/2109.01339
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3
Akademický článek
In-layer inhomogeneity of molecular dynamics in quasi-liquid layers of ice.
Autor: Yasuda, Ikki1 (AUTHOR), Endo, Katsuhiro1 (AUTHOR), Arai, Noriyoshi1 (AUTHOR), Yasuoka, Kenji1 (AUTHOR) yasuoka@mech.keio.ac.jp
Publikováno v: Communications Chemistry. 5/29/2024, Vol. 7 Issue 1, p1-11. 11p.
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4
Akademický článek
Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities.
Autor: Yasuda, Ikki1 (AUTHOR), Endo, Katsuhiro1 (AUTHOR), Yamamoto, Eiji2 (AUTHOR), Hirano, Yoshinori1,3 (AUTHOR), Yasuoka, Kenji1 (AUTHOR) yasuoka@mech.keio.ac.jp
Publikováno v: Communications Biology. 5/19/2022, Vol. 5 Issue 1, p1-9. 9p.
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5
Akademický článek
Graph-Neural-Network-Based Unsupervised Learning of the Temporal Similarity of Structural Features Observed in Molecular Dynamics Simulations.
Autor: Ishiai, Satoki, Yasuda, Ikki, Endo, Katsuhiro, Yasuoka, Kenji
Publikováno v: Journal of Chemical Theory & Computation; 1/23/2024, Vol. 20 Issue 2, p819-831, 13p
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7
Periodical
Relation in ligand dynamics and protein mutation using unsupervised machine learning
Autor: Yasuda, Ikki, Endo, Katsuhiro, Hirano, Yoshinori, Yamamoto, Eiji, Yasuoka, Kenji
Publikováno v: Biophysical Journal; February 2023, Vol. 122 Issue: 3, Number 3 Supplement 1 p182a-182a, 1p
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9
Akademický článek
Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 M pro .
Autor: Mustali J; Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy., Yasuda I; Department of Mechanical Engineering, Keio University Japan arai@mech.keio.ac.jp., Hirano Y; Department of Mechanical Engineering, Keio University Japan arai@mech.keio.ac.jp., Yasuoka K; Department of Mechanical Engineering, Keio University Japan arai@mech.keio.ac.jp., Gautieri A; Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy., Arai N; Department of Mechanical Engineering, Keio University Japan arai@mech.keio.ac.jp.
Publikováno v: RSC advances [RSC Adv] 2023 Nov 22; Vol. 13 (48), pp. 34249-34261. Date of Electronic Publication: 2023 Nov 22 (Print Publication: 2023).
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