Keisuke Yanagisawa

Associate Professor, Institute of Science Tokyo

Peer-reviewed Journal Papers

  1. Jun Koseki, Chie Motono, Keisuke Yanagisawa, Genki Kudo, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis”, Journal of Chemical Information and Modeling, 65: 5567-5575, 2025/6. DOI: 10.1021/acs.jcim.4c02111
  2. Chie Motono, Keisuke Yanagisawa, Jun Koseki, Kenichiro Imai, “CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction”, International Journal of Molecular Sciences, 26: 4710, 2025/5. DOI: 10.3390/ijms26104710
  3. Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama, “CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation”, Briefings in Bioinformatics, 25: bbae417, 2024/7. DOI: 10.1093/bib/bbae417
  4. Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “QUBO Problem Formulation of Fragment-Based Protein–Ligand Flexible Docking”, Entropy, 26: 397, 2024/4. DOI: 10.3390/e26050397
  5. Genki Kudo, Keisuke Yanagisawa, Ryunosuke Yoshino, Takatsugu Hirokawa, “AAp-MSMD: Amino Acid Preference Mapping on Protein–Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics”, Journal of Chemical Information and Modeling, 63: 7768-7777, 2023/12. DOI: 10.1021/acs.jcim.3c01677
  6. 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, 63: 2240-2250, 2023/4. DOI: 10.1021/acs.jcim.2c01573
  7. 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, 62: 4549-4560, 2022/9. DOI: 10.1021/acs.jcim.2c00931
  8. 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, 7: 30265-30274, 2022/8. DOI: 10.1021/acsomega.2c03470
  9. 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, 23: 4749, 2022/4. DOI: 10.3390/ijms23094749
  10. Kazuki Takabatake, Keisuke Yanagisawa, Yutaka Akiyama, “Solving Generalized Polyomino Puzzles Using the Ising Model”, Entropy, 24: 354, 2022/2. DOI: 10.3390/e24030354
  11. 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, 38: 1110-1117, 2022/1. DOI: 10.1093/bioinformatics/btab726
  12. 渓甫 柳澤, “タンパク質立体構造情報を用いた薬剤バーチャルスクリーニング”, JSBi Bioinformatics Review, 2: 76-86, 2021/9. DOI: 10.11234/jsbibr.2021.9
  13. Kazuki Takabatake, Kazuki Izawa, Motohiro Akikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Improved Large-Scale Homology Search by Two-Step Seed Search Using Multiple Reduced Amino Acid Alphabets”, Genes, 12: 1455, 2021/9. DOI: 10.3390/genes12091455
  14. Masatake Sugita, Satoshi Sugiyama, Takuya Fujie, Yasushi Yoshikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations”, Journal of Chemical Information and Modeling, 61: 3681-3695, 2021/7. DOI: 10.1021/acs.jcim.1c00380
  15. Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu, “EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation”, Journal of Chemical Information and Modeling, 61: 2744-2753, 2021/6. DOI: 10.1021/acs.jcim.1c00134
  16. Masahiro Mochizuki, Shogo D. Suzuki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “QEX: target-specific druglikeness filter enhances ligand-based virtual screening”, Molecular Diversity, 23: 11-18, 2019/2. DOI: 10.1007/s11030-018-9842-3
  17. Takashi Tajimi, Naoki Wakui, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques”, BMC Bioinformatics, 19: 527, 2018/12. DOI: 10.1186/s12859-018-2529-z
  18. Keisuke Yanagisawa, Shunta Komine, Rikuto Kubota, Masahito Ohue, Yutaka Akiyama, “Optimization of memory use of fragment extension-based protein–ligand docking with an original fast minimum cost flow algorithm”, Computational Biology and Chemistry, 74: 399-406, 2018/6. DOI: 10.1016/j.compbiolchem.2018.03.013
  19. Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions”, BMC Bioinformatics, 19: 62, 2018/5. DOI: 10.1186/s12859-018-2073-x
  20. Keisuke Yanagisawa, Shunta Komine, Shogo D Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Spresso: an ultrafast compound pre-screening method based on compound decomposition”, Bioinformatics, 33: 3836-3843, 2017/12. DOI: 10.1093/bioinformatics/btx178
  21. Shuntaro Chiba, Takashi Ishida, Kazuyoshi Ikeda, Masahiro Mochizuki, Reiji Teramoto, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, M. Michael Gromiha, Tatsuya Okuno, Koya Kato, Shintaro Minami, George Chikenji, Shogo D. Suzuki, Keisuke Yanagisawa, Woong-Hee Shin, Daisuke Kihara, Kazuki Z. Yamamoto, Yoshitaka Moriwaki, Nobuaki Yasuo, Ryunosuke Yoshino, Sergey Zozulya, Petro Borysko, Roman Stavniichuk, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima, “An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes”, Scientific Reports, 7: 12038, 2017/9. DOI: 10.1038/s41598-017-10275-4
  22. Shuntaro Chiba, Kazuyoshi Ikeda, Takashi Ishida, M. Michael Gromiha, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Kun-Yi Hsin, Hiroaki Kitano, Kazuki Yamamoto, Nobuyoshi Sugaya, Koya Kato, Tatsuya Okuno, George Chikenji, Masahiro Mochizuki, Nobuaki Yasuo, Ryunosuke Yoshino, Keisuke Yanagisawa, Tomohiro Ban, Reiji Teramoto, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, Philip Prathipati, Junichi Ito, Yuko Tsuchiya, Kenji Mizuguchi, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima, “Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target”, Scientific Reports, 5: 17209, 2015/11. DOI: 10.1038/srep17209
  23. Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama, “Drug Clearance Pathway Prediction Based on Semi-supervised Learning”, IPSJ Transactions on Bioinformatics, 8: 21-27, 2015/8. DOI: 10.2197/ipsjtbio.8.21

Peer-reviewed International Conferences

  1. Kazuki Takabatake, Kazuki Izawa, Motohiro Akikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Improved Homology Search for Metagenomic Analysis by Two-Step Seed Search with Reduced Amino Acid Alphabets”, The 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021), 2021/10/29–31.
  2. Kazuya Isawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Antisense oligonucleotide activity analysis based on opening and binding energies to targets”, In Proceedings of the 27th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’21), 2021/7/27.
  3. Masahito Ohue, Ryota Ii, Keisuke Yanagisawa, Yutaka Akiyama, “Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph”, In Proceedings of the 25th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’19), 2019/7/29.
  4. Takashi Tajimi, Naoki Wakui, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques”, The 29th International Conference on Genome Informatics (GIW 2018), 2018/12/4.
  5. Keisuke Yanagisawa, Shunta Komine, Rikuto Kubota, Masahito Ohue, Yutaka Akiyama, “Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm”, The 16th Asia Pacific Bioinformatics Conference (APBC2018), 2018/1/15.
  6. Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions”, The 16th Asia Pacific Bioinformatics Conference (APBC2018), 2018/1/15.
  7. Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “ESPRESSO: An ultrafast compound pre-screening method based on compound decomposition”, The 27th International Conference on Genome Informatics (GIW 2016), 2016/10/4.

International Conferences (without peer review)

Oral Presentation

  1. Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “FraSCO-VS: Fragment-based drug virtual screening by combinatorial optimization with quantum annealer”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.
  2. Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu, “Systematic construction of the cosolvents sets for cosolvent MD (CMD) with the large-scale simulation”, AHeDD2019/IPAB2019 Joint Symposium, 2019/11/29.

Poster Presentation

  1. Chie Motono, Keisuke Yanagisawa, Jun Koseki, Kenichiro Imai, “CrypTothML: Cryptic Site Prediction using Mixed-Solvent Molecular Dynamics Simulation and Machine Learning”, The international Chemical Congress of Pacific Basin Societies (Pacifichem) 2025, 2025/12/15.
  2. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Evaluation of Protein-Ligand Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.
  3. Masatake Sugita, Yudai Noso, Jianan Li, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “Protocol for Membrane Permeability Prediction of Cyclic Peptides by Combining Molecular Dynamics Simulations and Machine Learning”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.
  4. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “Enhancing virtual screening accuracy by refining docking calculation scoring with mixed-solvent molecular dynamics”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.
  5. Masahiro Shimizu, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “An Automatic Iterative Refinement Protocol for Restraint Parameters in REUS Molecular Dynamics”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.
  6. Masayoshi Shimizu, Satoshi Yoneyama, Keisuke Yanagisawa, Yutaka Akiyama, “Development of a fast pre-screening method using compound retrieval by fragment pose pairs”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.
  7. Chie Motono, Jun Koseki, Keisuke Yanagisawa, Genki Kudo, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “Cryptic site detection using machine learning based on mixed-solvent molecular dynamics simulations results”, Asia & Pacific Bioinformatics Joint Conference 2024, 2024/10/22.
  8. Jun Koseki, Chie Motono, Keisuke Yanagisawa, Genki Kudo, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “Development of the Cryptic Site searching method with Mixed-solvent molecular dynamics and Topological data analyses methods”, Asia & Pacific Bioinformatics Joint Conference 2024, 2024/10/22.
  9. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Evaluation of Protein-Compound Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, 21st IUPAB and 62nd BSJ joint congress 2024, 28P-189, 2024/6/28.
    * Presentation poster
  10. Masatake Sugita, Takuya Fujie, Yudai Noso, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Development and Application of a Protocol for Predicting Membrane Permeability of Cyclic Peptides Based on Molecular Dynamics Simulations”, 21st IUPAB and 62nd BSJ joint congress 2024, 26P-210, 2024/6/26.
  11. 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 Society 63rd Annual Meeting and 2792-Pos, 2019/3/02.
  12. Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Spresso: An ultrafast compound pre-screening method based on compound fragmentation”, Biophysical Society 62nd Annual Meeting, 2018/2/17.
  13. Rikuto Kubota, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Toward efficient protein-ligand docking for virtual screening by reuse of fragments”, The 16th Asia Pacific Bioinformatics Conference (APBC2018) and Poster C5, 2018/1/15.
  14. Keisuke Yanagisawa, Shunta Komine, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Fast pre-filtering for virtual screening based on compound fragmentation”, 3rd IIT Madras – Tokyo Tech Joint Symposium on Algorithms and Applications of Bioinformatics and P34, 2015/11/05.

Domestic Conferences (without peer review)

Oral Presentation

  1. Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “Frasco-VS: Quantum Annealing for Fragment-based Drug Candidate Screening”, IPSJ SIG Technical Report, 2026-QS-17(5): 1–8, 2026/3/16. [link]
  2. Masahiro Shimizu, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “Development of an Automatic Parameter Tuning Method for REST/REUS Molecular Dynamics”, IPSJ SIG Technical Report, 2026-BIO-84(26): 1–8, 2026/3/13. [link]
  3. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Compound Substructure Profiling by Inverse MSMD and Its Application to Binding Affinity Prediction”, IPSJ SIG Technical Report, 2026-BIO-84(19): 1–8, 2026/3/13. [link]
  4. Masayoshi Shimizu, Satoshi Yoneyama, Keisuke Yanagisawa, Yutaka Akiyama, “COFFEE-PRESC: A pre-screening method using compound retrieval by promising fragment pairs”, IPSJ SIG Technical Report, 2026-BIO-84(3): 1–8, 2026/3/12. [link]
  5. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “Enhancing virtual screening accuracy by improving scoring of docking calculations using mixed-solvent molecular dynamics”, IPSJ SIG Technical Report, 2026-BIO-84(4): 1–8, 2026/3/12. [link]
  6. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “Probe atom distributions obtained from mixed-solvent molecular dynamics improves scoring of docking calculations”, IPSJ SIG Technical Report, 2025-BIO-82(43): 1–8, 2025/6/22.
  7. Satoshi Yoneyama, Keisuke Yanagisawa, Yutaka Akiyama, “Selection of Representative Fragment Sets for Fragment-Based Virtual Screening Focusing on 3D Structural Similarity”, IPSJ SIG Technical Report, 2025-BIO-82(44): 1–8, 2025/6/22.
  8. Masahiro Shimizu, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “Development of a replica parameter optimization method for REUS MD and its application to membrane permeability prediction of cyclic peptides”, IPSJ SIG Technical Report, 2025-BIO-82(45): 1–8, 2025/6/22.
  9. Ryoya Nakano, Keisuke Yanagisawa, Yutaka Akiyama, “Application of the Ising model to fragment-based protein-ligand docking”, IPSJ SIG Technical Report, 2025-BIO-82(46): 1–8, 2025/6/22.
  10. Tomoya Saito, Masayoshi Shimizu, Keisuke Yanagisawa, Yutaka Akiyama, “フラグメントの類似性を考慮した化合物立体配座検索システムの構築”, IPSJ SIG Technical Report, 2025-BIO-81(12): 1–8, 2025/3/7.
  11. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “共溶媒分子動力学法による化合物ドッキング向けのバイアス情報の取得”, IPSJ SIG Technical Report, 2024-BIO-78(37): 1–8, 2024/6/21.
  12. Masayoshi Shimizu, Keisuke Yanagisawa, Yutaka Akiyama, “Development of a compound pre-screening method based on spatial arrangement of promising fragment pairs”, IPSJ SIG Technical Report, 2024-BIO-78(38): 1–8, 2024/6/21.
  13. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Inverse MSMDシミュレーションによるタンパク質－化合物部分構造相互作用定量的評価手法の開発”, 第24回日本蛋白質科学会年会: [WS-14] バイオインフォマティクスと農芸化学の出会うところ, 2024/6/13.
  14. Masatake Sugita, Takuya Fujie, Yudai Noso, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “拡張アンサンブル分子動力学シミュレーションに基づいた環状ペプチドの膜透過性予測技術の開発と応用”, 第24回日本蛋白質科学会年会: [WS-3] ペプチド設計の現在と未来, 2024/6/11.
  15. Ayako Nunobe, Keisuke Yanagisawa, Yutaka Akiyama, “フラグメントに基づくバーチャルスクリーニングへの利用などを目指したフラグメント集合の選定”, IPSJ SIG Technical Report, 2024-BIO-77(31): 1–7, 2024/3/8.
  16. Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama, “CycPeptMP：マルチレベルの分子特徴とデータ拡張による環状ペプチドの膜透過性予測手法の開発”, IPSJ SIG Technical Report, 2024-BIO-77(15): 1–8, 2024/3/7.
  17. Yudai Noso, Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “分子動力学シミュレーション軌跡データから抽出した位置依存特徴量を活用した環状ペプチドの膜透過性予測”, IPSJ SIG Technical Report, 2024-BIO-77(16): 1–8, 2024/3/7.
  18. Keisuke Yanagisawa, “薬剤設計のためにはAlphaFoldはまだまだ足りない”, 第12回生命医薬情報学連合大会 (IIBMP2023): [WS-2] バイオインフォマティクスの8の問題, 2023/9/5.
  19. Ginga Watanabe, Keisuke Yanagisawa, Yutaka Akiyama, “標的RNAの高次構造予測に基づく低活性ASO候補配列の推測”, IPSJ SIG Technical Report, 2023-BIO-74(36): 1–8, 2023/7/1.
  20. Tomoya Saito, Keisuke Yanagisawa, Yutaka Akiyama, “フラグメント対の相対位置から検索可能な化合物立体配座データベースの構築”, IPSJ SIG Technical Report, 2023-BIO-74(37): 1–8, 2023/7/1.
  21. Jianan Li, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Masahito Ohue, Yutaka Akiyama, “CycPeptMPDB：包括的な環状ペプチド膜透過率データベースの開発”, IPSJ SIG Technical Report, 2023-BIO-74(38): 1–8, 2023/7/1.
  22. Keisuke Yanagisawa, Rikuto Kubota, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “REstretto: An efficient protein-ligand docking tool based on a fragment reuse strategy”, CBI学会2022年大会, O2-1, 2022/10/25.
    * Presentation slide
  23. 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”, CBI学会2022年大会, O3-2, 2022/10/25.
  24. Yudai Noso, Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “分子動力学シミュレーション軌跡データからの環状ペプチドの膜透過性と相関が高い特徴量の抽出”, IPSJ SIG Technical Report, 2022-BIO-70(50): 1–8, 2022/6/29.
  25. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “インバース共溶媒分子動力学法による分子プローブ周辺残基環境の可視化”, 第22回日本蛋白質科学会年会, O7-12, 2022/6/7.
  26. Masaya Inagaki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “フラグメント化された化合物立体構造データベースの構築”, IPSJ SIG Technical Report, 2022-BIO-69(15): 1–8, 2022/3/11.
  27. Yuki Tsushima, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “新たなデータセットによる長距離フラグメントリンキング手法の再評価”, IPSJ SIG Technical Report, 2022-BIO-69(16): 1–8, 2022/3/11.
  28. Shu Tamano, Kazuki Izawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Gapmer型ASOにおけるオフターゲット効果のリスク評価手法の提案”, IPSJ SIG Technical Report, 2022-BIO-69(7): 1–7, 2022/3/10.
  29. Mahiro Yamazaki, Kazuki Izawa, Ryo Hirata, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “結合エネルギーを考慮したゲノムワイドな高速短鎖核酸配列検索手法の開発”, IPSJ SIG Technical Report, 2022-BIO-69(8): 1–8, 2022/3/10.
  30. Masatake Sugita, Satoshi Sugiyama, Takuya Fujie, Yasushi Yoshikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “分子動力学シミュレーションに基づいた環状ペプチドの膜透過率の大規模予測”, 第58回日本生物物理学会年会, 2-03-1712, 2021/11/25.
  31. Masatake Sugita, Satoshi Sugiyama, Takuya Fujie, Yasushi Yoshikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Large-scale membrane permeability prediction of cyclic peptides crossing a lipid bilayer based on enhanced sampling molecular dynamics simulations”, CBI学会2021年大会, O2-1, 2021/10/26.
  32. Yuki Tsushima, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “タンパク質表面との結合親和性を考慮した長距離フラグメントリンキング手法の開発”, IPSJ SIG Technical Report, 2021-BIO-67(1): 1–8, 2021/9/30.
  33. Kazuya Isawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “標的配列との結合・開放エネルギー推定に基づくアンチセンス核酸の阻害活性モデルの研究”, IPSJ SIG Technical Report, 2020-BIO-65(7): 1–7, 2021/3/11.
  34. Keisuke Yanagisawa, “共溶媒分子動力学シミュレーションにおける創薬向け共溶媒セットの構築”, The 43rd Annual Meeting of the Molecular Biology Socienty of Japan (MBSJ2020): [2F-11] forum “State-of-the-art researches of in silico drug discovery, 2020/12/3.
  35. Rikuto Kubota, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “共通な部分構造の再利用による高速なタンパク質リガンドドッキング手法の開発”, IPSJ SIG Technical Report, 2019-BIO-61(3): 1–8, 2020/3/12.
  36. Masahito Ohue, Ryota Ii, Keisuke Yanagisawa, Yutaka Akiyama, “Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph”, IPSJ SIG Technical Report, 2019-MPS-124(3): 1–4, 2019/7/29.
  37. Ryota Ii, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “分子グラフ上の距離を考慮したグラフ畳込みニューラルネットワークによる化合物活性予測”, IPSJ SIG Technical Report, 2018-BIO-57(11): 1–8, 2019/3/9.
  38. Rikuto Kubota, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “共通な部分構造の再利用アルゴリズムを用いたタンパク質リガンドドッキング手法の開発”, IPSJ SIG Technical Report, 2018-BIO-54(42): 1–7, 2018/6/15.
  39. Keisuke Yanagisawa, Shunta Komine, Rikuto Kubota, Masahito Ohue, Yutaka Akiyama, “フラグメント伸長型化合物ドッキング計算のための重み付きオフラインキャッシュ問題の厳密解アルゴリズム”, IPSJ SIG Technical Report, 2017-BIO-50(38): 1–8, 2017/6/25.
  40. Keisuke Yanagisawa, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “標的タンパク質の立体構造を用いたリガンド候補化合物の上限サイズの推定による化合物フィルタリング”, IPSJ SIG Technical Report, 2016-BIO-49(6): 1–7, 2017/3/24.
  41. Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “フラグメント分割に基づく超高速化合物プレスクリーニング手法 ESPRESSO”, IPSJ SIG Technical Report, 2016-BIO-46(18): 1–7, 2016/7/5.
  42. Shogo D. Suzuki, Keisuke Yanagisawa, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “SVMとDeep Learningに基づくヒトc-Yesキナーゼ阻害化合物の予測”, IPSJ SIG Technical Report, 2015-BIO-42(36): 1–7, 2015/6/24.
  43. Keisuke Yanagisawa, Takashi Ishida, Yuichi Sugiyama, Yutaka Akiyama, “Drug clearance pathway prediction based on semi-supervised learning”, IPSJ SIG Technical Report, 2014-BIO-41(11): 1–6, 2015/3/20.
  44. Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama, “半教師付き学習を用いた薬物クリアランス経路予測”, IPSJ SIG Technical Report, 2014-BIO-38(10): 1–6, 2014/6/26.

Poster Presentation

  1. Kaoru Tomita, Keisuke Yanagisawa, Yutaka Akiyama, “量子アニーリングを用いたブラックボックス最適化における事前知識導入による学習効率改善の評価”, Quantum Annealing Workshop 2026, P12, 2026/2/18-19.
  2. Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “Frasco-VS：フラグメントに基づく薬剤候補化合物選抜の量子アニーラによる実現”, Quantum Annealing Workshop 2026, P14, 2026/2/18-19.
  3. Masahiro Shimizu, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “Development of an automatic parameter adjustment method for REST/REUS MD and its application to predicting the membrane permeability of cyclic peptides”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P01-17, 2025/10/27.
  4. Ryoya Nakano, Keisuke Yanagisawa, Yutaka Akiyama, “Improvement of fragment-based protein–ligand docking using the Quantum Annealer”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P06-19, 2025/10/27.
  5. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “Enhancing virtual screening accuracy by refining docking calculation scoring with mixed-solvent molecular dynamics”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P06-16, 2025/10/27.
  6. 米山 慧, Keisuke Yanagisawa, Yutaka Akiyama, “Construction of representative fragment sets based on mutual 3D structural similarity and docking feasibility for fragment-based virtual screening”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P06-15, 2025/10/27.
  7. Masayoshi Shimizu, Keisuke Yanagisawa, Yutaka Akiyama, “COFFEE-PRESC: a fast pre-screening method using chemical compound retrieval by fragment pose pairs”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P06-13, 2025/10/27.
  8. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Estimation of Protein-Ligand Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P01-26, 2025/10/27.
  9. Masatake Sugita, Kei Terakura, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “Analysis of membrane permeation processes of cyclic peptides on multiple reaction coordinates based on the Markov state model”, Chem-Bio Informatics Society(CBI) Annual Meeting 2025, P01-06, 2025/10/27.
  10. Masatake Sugita, Kei Terakura, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “Markov state Model に基づいた環状ペプチド膜透過過程の多次元の反応座標における速度論的な解析”, The 63rd Annual Meeting of The Biophysical Society of Japan, 2Pos175, 2025/9/24.
  11. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “共溶媒分子動力学法におけるプローブ原子分布を活用したドッキング計算のスコアリングの改良”, The 25th Annual Meeting of the Protein Science Society of Japan, 1P-061, 2025/6/18.
  12. Masahiro Shimizu, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “REUS MDのレプリカパラメータ最適化手法の開発と環状ペプチド膜透過性予測への応用”, The 25th Annual Meeting of the Protein Science Society of Japan, 1P-064, 2025/6/18.
  13. Kei Terakura, Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “環状ペプチドの膜透過過程の Markov state Model に基づいた速度論的な解析”, The 25th Annual Meeting of the Protein Science Society of Japan, 2P-044, 2025/6/18.
  14. Chie Motono, Keisuke Yanagisawa, 小関 準, Kenichiro Imai, “CrypTothML: 共溶媒分子動力学計算と機械学習を組み合わせたクリプティックサイト予測手法”, The 25th Annual Meeting of the Protein Science Society of Japan, 3P-038, 2025/6/18.
  15. Masatake Sugita, 能祖 雄大, 李 佳男, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “分子動力学シミュレーションと機械学習を組み合わせた環状ペプチド膜透過性の予測法の開発”, The 25th Annual Meeting of the Protein Science Society of Japan, 3P-040, 2025/6/18.
  16. Genki Kudo, Keisuke Yanagisawa, Ryunosuke Yoshino, Takatsugu Hirokawa, “共溶媒分子動力学法を用いたタンパク質－タンパク質相互作用プロファイル”, 第52回構造活性相関シンポジウム, KP20, 2024/12/12.
  17. Kei Terakura, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “Kinetic Analysis of Membrane Permeation Process of Cyclic Peptides Using Markov State Models with Molecular Dynamics Simulations”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P01-12, 2024/10/28.
  18. Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama, “CycPeptMP: Development of Membrane Permeability Prediction Model of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P03-11, 2024/10/28.
  19. Jun Koseki, Chie Motono, Keisuke Yanagisawa, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “Development of the Cryptic Site searching method with Mixed-solvent molecular dynamics and Topological data analyses methods”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P04-03, 2024/10/28.
  20. Masatake Sugita, Yudai Noso, Takuya Fujie, Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama, “Development of Prediction Models for Membrane Permeability of Cyclic Peptides using 3D Descriptors obtained from Molecular Dynamics Simulations and 2D Descriptors”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P07-05, 2024/10/28.
  21. Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “QUBO Problem Formulation of Fragment-Based Protein–Compound Flexible Docking”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P07-14, 2024/10/28.
    * Presentation poster
  22. Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “Acquisition of Bias Information for Protein-Ligand Docking by Mixed-Solvent Molecular Dynamics”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P07-15, 2024/10/28.
    * Like! Poster Award 受賞（学会参加者投票による選出）
  23. Masayoshi Shimizu, Keisuke Yanagisawa, Yutaka Akiyama, “Development of a compound pre-screening method based on docking of fragments”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P07-16, 2024/10/28.
  24. Tomoya Saito, Keisuke Yanagisawa, Yutaka Akiyama, “Development of an efficient compound 3D conformer search system based on relative position of fragments”, Chem-Bio Informatics Society(CBI) Annual Meeting 2024, P07-33, 2024/10/28.
  25. Chie Motono, Keisuke Yanagisawa, Genki Kudo, Takatsugu Hirokawa, Kenichiro Imai, “共溶媒分子動力学シミュレーションによるクリプティックサイト予測”, The 24th Annual Meeting of the Protein Science Society of Japan, 3P-063, 2024/6/11.
  26. Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Development of a Protocol for Predicting Membrane Permeability of Cyclic Peptides Based on Molecular Dynamics Simulations”, The 61st Annual Meeting of The Biophysical Society of Japan, 2Pos183, 2023/11/14.
  27. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Evaluation of Protein-Chemical Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, The 61st Annual Meeting of The Biophysical Society of Japan, 2023/11/14.
  28. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Estimation of Protein-Chemical Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, Chem-Bio Informatics Society(CBI) Annual Meeting 2023, 2023/10/23.
  29. Genki Kudo, Keisuke Yanagisawa, Ryunosuke Yoshino, Takatsugu Hirokawa, “Amino Acid Preference Mapping on Protein-Protein Interaction Surface using Mixed-Solvent Molecular Dynamics”, Chem-Bio Informatics Society(CBI) Annual Meeting 2022, P02-04, 2022/10/25.
  30. Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “インバース共溶媒分子動力学法による分子プローブ周辺アミノ酸残基環境の可視化”, The 60th Annual Meeting of The Biophysical Society of Japan, 1Pos031, 2022/9/28.
    * Presentation Poster
  31. Masatake Sugita, Satoshi Sugiyama, Takuya Fujie, Yasushi Yoshikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “分子動力学シミュレーションに基づいた環状ペプチドの膜透過率の大規模予測”, 43rd Symposium on Solution Chemistry of Japan, P38, 2021/10/29.
  32. Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu, “Systematic construction of the cosolvents sets for cosolvent MD (CMD) with the large-scale computation”, Chem-Bio Informatics Society(CBI) Annual Meeting 2019, P1-24, 2019/10/22.
  33. Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu, “Estimation of the probability map (Pmap) similarity of cosolvent MD (CMD) from structural similarities of cosolvents”, The 57th Annual Meeting of The Biophysical Society of Japan, 1Pos012, 2019/9/24.
  34. Juanjuan Lu, Keisuke Yanagisawa, Takashi Ishida, “Development of a novel linear notation of chemical compounds for deep learning”, Chem-Bio Informatics Society(CBI) Annual Meeting 2018, P5-17, 2018/10/9.
  35. Ryota Ii, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “大域的化合物特徴を表現するグラフ畳み込みネットワーク”, Informatics in Biology, Medicine and Pharmacology 2018 (IIBMP2018), P-76, 2018/9/19.
  36. Rikuto Kubota, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Development of efficient protein-ligand docking method for virtual screening by reuse of fragments”, 1st RWBC-OIL Workshop, Poster no. 18, 2018/5/8.
  37. Masahito Ohue, Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Yutaka Akiyama, “MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions”, Informatics in Biology, Medicine and Pharmacology 2017 (IIBMP2017), P57, 2017/9/27.
  38. Masahito Ohue, Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Yutaka Akiyama, “MEGADOCK-WEB: タンパク質間相互作用予測の統合データベース”, 第55回日本生物物理学会年会, 3Pos174, 2017/9/21.
  39. Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “ESPRESSO: An ultrafast compound pre-screening method with segmented compounds”, Chem-Bio Informatics Society(CBI) Annual Meeting 2016, P2-19, 2016/10/25.
  40. Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “ESPRESSO: An ultrafast compound pre-screening method based on compound segmentation”, Informatics in Biology, Medicine and Pharmacology 2016 (IIBMP2016), P65, 2016/9/29.
  41. Keisuke Yanagisawa, Shunta Komine, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Fast pre-filtering for virtual screening based on ligand decomposition”, 第21回 創剤フォーラム若手研究会, P-6, 2015/11/28.
  42. Keisuke Yanagisawa, Shunta Komine, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Fast pre-filtering for virtual screening based on compound decomposition”, Informatics in Biology, Medicine and Pharmacology 2015 (IIBMP2015), 2015/10/29.

Books

  1. 田中 成典, 広川 貴次, 池口 満徳 監修, インシリコ創薬 計算創薬の基礎から実例まで, 森北出版, 2025
    * An textbook of in silico drug design
  2. 金森 敬文 監訳, データサイエンスと機械学習 理論からPythonによる実装まで, 東京化学同人, 2022
    * Japanese translation of: Kroese DP, et al., Data Science and Machine Learning: Mathematical and Statistical Methods, CRC Press, 2019.

Patents

Grants

  1. “A virtual screening method using representative fragment-based reduced compound libraries”, JSPS KAKENHI (Grant-in-Aid for Scientific Research (B)) 25K03215, Co-investigator (PI: Yutaka Akiyama), 18 850 000 yen (in total), 2025/04-2030/03.
  2. “共溶媒分子動力学法によるタンパク質化合物ドッキング計算のスコア関数の改善”, Research Funding Program, Kayamori Foundation of Informational Science Advancement, PI, 1 000 000 yen, 2024/10-2026/09.
  3. “Development of large-scale virtual screening method for quantum/AI hybrid drug discovery”, NEDO Development of Quantum-Classical Hybrid Use-Case Technologies in Cyber-Physical Space, Co-investigator (PI: Motokazu Iwasaki), 224 587 000 yen (in total), 2023/08-2026/03.
  4. “Designing cyclic peptide with target protein selection based on mixed-solvent molecular dynamics simulation”, JSPS KAKENHI (Grant-in-Aid for Scientific Research (B)) 23H03495/23K28185, PI, 18 590 000 yen, 2023/04-2027/03.
  5. “Recognition of protein surface where suitable for ligand binding with Inverse-MSMD simulations”, Grant for young researcher, School of Computing, Tokyo Institute of Technology, PI, 500 000 yen, 2022/06-2023/03.
  6. “Development of chemical substructure-based virtual screening method for huge compound library”, JSPS KAKENHI (Grant-in-Aid for Scientific Research (B)) 22H03684/23K24939, Co-investigator (PI: Yutaka Akiyama), 17 030 000 yen (in total), 2022/04-2025/03.
  7. “Computer-aided lead optimization with cosolvent molecular dynamics (CMD)”, Grant for young researcher, School of Computing, Tokyo Institute of Technology, PI, 496 000 yen, 2020/10-2021/03.
  8. “Comprehensive prediction of cryptic binding sites by multi-task deep learning”, JSPS KAKENHI (grant-in-aid for young reseachers) 20K19917, PI, 4 290 000 yen, 2020/04-2023/03.
  9. “Improvement of cosolvent MD which enables the systematic search of binding sites and the novel screening way of drug candidates”, JSPS KAKENHI (grant-in-aid for JSPS fellows) 19J00878, PI, 1 820 000 yen, 2019/04-2020/03.
  10. “Development of divide-and-conquer based docking method using common partial structures of hundreds of millions of compounds”, JSPS KAKENHI (grant-in-aid for JSPS fellows) 17J06897, PI, 2 100 000 yen, 2017/04-2019/03.

Computation resources, etc.

  1. Yutaka Akiyama, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie. “多次元の反応座標に基づく環状ペプチド膜透過過程の詳細な解析法の確立”, TSUBAME Grand Challenge Program, 2025/06/23-2025/06/30.
  2. Yutaka Akiyama, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Kei Terakura. “２次元レプリカ交換法を用いた大規模シミュレーションに基づく膜透過性予測困難ペプチドの膜透過メカニズムの詳細な解析”, TSUBAME Grand Challenge Program, 2024/05/27-2024/06/03.
  3. Yutaka Akiyama, Masahito Ohue, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Yudai Noso. “Detailed analysis of membrane permeation mechanisms of difficult-to-predict membrane permeability peptides based on large-scale simulations using a two-dimensional replica exchange method”, TSUBAME Grand Challenge Program, 2023/05/10-2023/05/17.
  4. Yutaka Akiyama, Masahito Ohue, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Yudai Noso. “Development of a method to accelerate convergence of large-scale simulations of membrane permeation processes of cyclic peptides based on a two-dimensional replica exchange method”, TSUBAME Grand Challenge Program, 2022/06/08-2022/06/15.
  5. Yutaka Akiyama, Keisuke Yanagisawa, Ohue Masahito, Yasushi Yoshikawa, Masatake Sugita, Takuya Fujie, Kento Aoyama, Satoshi Sugiyama. “Membrane permeability prediction of more than 100 cyclic peptides by REUS simulations and machine learning”, “ABCI Grand Challenge” Program, National Institute of Advanced Industrial Science and Technology (AIST), 2020/08/26-2020/08/27.
  6. Keisuke Yanagisawa. “Construction of the optimal cosolvents sets for cosolvent MD (CMD) with the large-scale computation”, Initiative on Promotion of Supercomputing for Young or Women Researchers, Information Technology Center, The University of Tokyo, $950, 2019/10/01-2020/03/31.

Awards

  1. 2019 Education Award of Excellence, Tokyo Institute of Technology (2021/03/02)
    * Yoshihiro Miyake, Naoaki Okazaki, Takefumi Kanamori, Tsuyoshi Murata, Shin-ya Nishizaki, Kazuyuki Shudo, Kenji Kise, Masamichi Shimosaka, Masakazu Sekijima, Keisuke Yanagisawa, Masahiro Kuze, Mitsuji Sampei, Ichiro Yamanaka, Takehiko Itoh, Toru Takeuchi, Takeo Yamaguchi, Kei Sakaguchi. “Progressive Graduate Minor in Data Science and Artificial Intelligence”
  2. Seiichi Tejima Doctoral Dissertation Award (2020/02/27)
  3. 4th Computer-Aided Drug Discovery Contest Grand Prize (Schrödinger K.K. Prize) (2017/12/15)
    * Team “DD-friends” (Yusuke Matsuyama, Takanori Hayashi, Ryota Ii, Toshitaka Tanebe, Takashi Tajimi, Rin Sato, Hikaru Ikeda, Keisuke Yanagisawa) : “Compound virtual screening by hybridization of structure- and ligand-based methods”
  4. 2nd Computer-Aided Drug Discovery Contest Student Encouragement Prize (2015/07/17)
    * Team “DEDENNE” (Shogo D. Suzuki, Keisuke Yanagisawa) : “c-Yes kinase inhibitor prediction with DEDENNE: Druggability Estimator by Deep Neural Network”
  5. 2014 SIGBIO Best Student Presentation Award, IPSJ SIGBIO (2015/06/25) [link]
    * Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama. “Drug clearance pathway prediction based on semi-supervised learning”, IPSJ Transactions on Bioinformatics, 8: 21-27, 2015/08. DOI: 10.2197/ipsjtbio.8.21
  6. 1st Computer-Aided Drug Discovery Contest Student Encouragement Prize (2014/07/17)
    * Team “Tokyo Tech TSUBAME” (Nobuaki Yasuo, Ryunosuke Yoshino, Keisuke Yanagisawa, Tomohiro Ban) : “Compound screening with constraint docking”
  7. 2013 Tokyo Institute of Technology Academic Excellence Awards, Tokyo Institute of Technology (2014/03/26)
  8. Bronze Prize, 2009 High School Chemistry Grand Prix (2009/09/26)

Others

  1. 佐藤 朋広, 井上 貴央, 柳澤 渓甫. “CBI学会2024年大会 大会報告 - 口頭発表 O06 データサイエンス -2- / 臨床インフォマティクス”, CBI学会誌, 13(1): 19, 2025/03. (in Japanese)
  2. 柳澤 渓甫. “共溶媒分子動力学における薬剤設計向け共溶媒セットの構築”, スーパーコンピューティングニュース, 23: 46-49, 2021/07. (in Japanese)
  3. 柳澤 渓甫. “「なんで、私が留学に！？」から始まる留学”, JSBi news letter, 38, 4-5, 2020/08. (in Japanese)
  4. Keisuke Yanagisawa. “情報工学から考える創薬”, 2nd Tokyo Bioinformatics Meeting, 2018/08/07. (Oral presentation, in Japanese)
  5. Keisuke Yanagisawa. “Spresso - 高速なタンパク質立体構造ベース創薬を目指して”, 1st Tokyo Bioinformatics Meeting, 2017/08/09. (Oral presentation, in Japanese)
  6. Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama. “フラグメント分割による超高速化合物プレスクリーニング手法 ESPRESSO”, 第56回 生物物理若手の会 夏の学校, 2016/09/02-05. (poster session, in Japanese)
  7. Keisuke Yanagisawa, Shunta Komine, Masahito Ohue, Takashi Ishida, Yutaka Akiyama. “Fast pre-docking method based on compound structure fragmentation”, ACLS International Summer School 2015, 2015/08/30-09/06. (poster session)
  8. Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama. “Drug clearance pathway prediction using semi-supervised learning”, ACLS International Summer School 2014, 2014/08/13-19. (poster session)

Theses