Rchr
J-GLOBAL ID：202101018621155610
Update date: May. 12, 2024
Yanagisawa Keisuke
ヤナギサワ ケイスケ | Yanagisawa Keisuke
Affiliation and department：
Job title：
Assistant Professor
Research theme for competitive and other funds (4)：
- 2022 - 2025 Virtual screening method for large-scale compound databases utilizing the commonality of partial structures
- 2020 - 2023 Comprehensive prediction of cryptic binding sites by multi-task deep learning
- 2019 - 2022 Improvement of cosolvent MD which enables the systematic search of binding sites and the novel screening way of drug candidates
- 2017 - 2019 Development of divide-and-conquer based docking method using common partial structures of hundreds of millions of compounds
Papers (16)：
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Jianan Li, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Masahito Ohue, Yutaka Akiyama. CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides. Journal of chemical information and modeling. 2023. 63. 7. 2240-2250
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Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama. Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations. Journal of chemical information and modeling. 2022. 62. 18. 4549-4560
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Keisuke Yanagisawa, Rikuto Kubota, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama. Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation. ACS omega. 2022. 7. 34. 30265-30274
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Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa. Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes. International journal of molecular sciences. 2022. 23. 9
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Jianan Li, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama. Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning. Bioinformatics (Oxford, England). 2022. 38. 4. 1110-1117
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MISC (14)：
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井澤和也, 柳澤渓甫, 柳澤渓甫, 大上雅史, 大上雅史, 秋山泰, 秋山泰. Inhibitory Activity Model of Antisense Oligonucleotide Based on Estimation of Binding and Opening Energies to Target Sequences. 情報処理学会研究報告(Web). 2021. 2021. BIO-65
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井澤和也, 井澤和也, 柳澤渓甫, 柳澤渓甫, 大上雅史, 大上雅史, 秋山泰, 秋山泰. Antisense oligonucleotide activity analysis based on opening and binding energies to targets. 情報処理学会研究報告(Web). 2021. 2021. MPS-134
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久保田陸人, 柳澤渓甫, 吉川寧, 大上雅史, 秋山泰. Development of an efficient protein-ligand docking method by reuse of fragments. 情報処理学会研究報告(Web). 2020. 2020. BIO-61
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Masahito Ohue, Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Yutaka Akiyama. Megadock-Web: An Integrated Database of High-Throughput Structure-Based Protein-Protein Interaction Predictions. BIOPHYSICAL JOURNAL. 2019. 116. 3. 563A-563A
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伊井良太, 柳澤渓甫, 大上雅史, 秋山泰. Graph convolutional neural networks considering distance on molecular graph for compound activity prediction. 情報処理学会研究報告(Web). 2019. 2019. BIO-57
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