化学×AI技術で世界を変える研究室

Publication

Publication

Research articles

2022

  1. Nakano H., Miyao T., “Visualization of Topological Pharmacophore Space with Graph Edit Distance”, ACS Omega, 7, 16, 14057-14068, 2022.
  2. Maeda I., Sato A., Tamura S., Miyao T., “Ligand‑based approaches to activity prediction for the early stage of structure–activity–relationship progression”, J. Comput. Aided Mol. Des., 36, 237–252, 2022.
  3. Nonoguchi Y., Miyao T., Goto C., Kawai T., Funatsu K., “Governing Factors for Carbon Nanotube Dispersion in Organic Solvents Estimated by Machine Learning “, Adv. Mater. Interfaces,  6, 2101723, 2022.
  4. Sato A., Miyao T., Funatsu K., “Prediction of Reaction Yield for Buchwald-Hartwig Cross coupling Reactions Using Deep Learning”, Molecular Informatics, 41, 2100156, 2022.

2021

  1. Tamura S., Jasial S., Miyao T., Funatsu K., “Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel”, Molecules, 39, 2000103, 2021.
  2. Nakano H., Miyao T., Jasial S., Funatsu K., “Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping”, J. Chem. Inf. Model., 61, 7, 3348-3360, 2021.
  3. Matsumoto K., Miyao T., Funatsu K., “Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration”, ACS Omega, 6, 18, 11964-11973, 2021.
  4. Sato A., Miyao T., Jasial S., Funatsu K., “Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations”, J. Comput. Aided Mol. Des., 35, 179-193, 2021.

2020

  1. Tamura S., Miyao T., Funatsu K., “Ligand-based Activity Cliff Prediction Models with Applicability Domain”, Molecular Informatics, 39, 2000103, 2020.
  2. Hikosaka T., Aoshima S., Miyao T., Funatsu K., “Soft Sensor Modeling for Identifying Significant Process Variables with Time Delays”, Ind. Eng. Chem. Res., 59, 26, 12156-12163, 2020.
  3. Nakano H., Miyao T., Funatsu K., “Exploring Topological Pharmacophore Graphs for Scaffold Hopping”, J. Chem. Inf. Model., 60, 4, 2073-2081, 2020.

2019

  1. Aoshima S., Miyao T., Funatsu K., “Soft-sensor modeling for semi-batch chemical process using limited number of sampling”, J. Comput. Aided Chem., 20, 119-132, 2019.
  2. Miyao T., Jasial S., Bajorath J., Funatsu K., “Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships”, J. Comput. Aided Mol. Des., 33, 8, 729-743, 2019.
  3. Laufkötter O., Miyao T., Bajorath J., “Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents”, ACS Omega, 4, 12, 15304–15311, 2019.
  4. Tamura S., Miyao T., Funatsu K., “Development of R-Group Fingerprints Based on the Local Landscape from an Attachment Point of a Molecular Structure”, J. Chem. Inf. Model., 59, 6, 2656–2663, 2019.
  5. Miyao T., Funatsu K., “Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties” J. Chem. Inf. Model., 59, 6, 2626–2641, 2019.
  6. Miyao T., Funatsu K., Bajorath J., “Three-dimensional activity landscape models of different design and their application to compound mapping and potency prediction”, J. Chem. Inf. Model., 59, 993-1004, 2019.
  7. Miyao T., Funatsu K., Bajorath J., “Exploring alternative strategies for the identification of potent compounds using support vector machine and regression modeling”, J. Chem. Inf. Model., 59, 983-992, 2019.

2018

  1. Miyao T., Bajorath J., “Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching”, J. Comput. Aided Mol. Des., 32, 759-767, 2018.
  2. Yonchev D., Vogt M., Stumpfe D., Kunimoto R., Miyao T., Bajorath J., “Computational assessment of chemical saturation of analog series under varying conditions”, ACS Omega, 3, 15799-15808, 2018.

受賞

  1. 船津公人, 日本化学会, Jan, 2021, 第38回学術賞
  2. 佐藤彰准, 日本コンピュータ化学会2020秋季年会, Nov, 2020, 日本コンピュータ化学会奨学賞
  3. 青島慎一郎, The 18th Asian Pacific Confederation of Chemical Engineering Congress(APCChE), Sep, 2019, Excellent Poster Award
  4. 宮尾知幸, 第41回ケモインフォマティクス討論会, Oct, 2018, 最優秀講演賞(若手研究者部門)

Conferences / Symposiums

  1. 前田樹, 佐藤彰准, 田村峻佑, 宮尾知幸, 日本薬学会第142年会, Mar, 2022. “インシリコスクリーニングのための不均衡データにより構築した機械学習手法の比較”
  2. 佐藤彰准, 宮尾知幸, 第44回ケモインフォマティクス討論会, Dec, 2021. “深層学習を用いた化学反応の収率予測モデルの構築”
  3. 前田樹, 田村峻佑, 佐藤彰准, 宮尾知幸,  日本コンピュータ化学会2021春季年会, Jun, 2021. “仮想スクリーニングにおける多数の不活性化合物の効率的利用”
  4. 佐藤彰准, 宮尾知幸, Swarit Jasial, 船津公人, 第43回ケモインフォマティクス討論会, Dec, 2020. “二次元分子表現と三次元分子表現を用いたQSAR/QSPRモデルの予測能力の比較”
  5. 佐藤彰准, 宮尾知幸, 船津公人, 日本コンピュータ化学会2020秋季年会, Nov, 2020. “深層学習を用いたBuchwald-Hartwigクロスカップリング反応の収率予測”
  6. 宮尾知幸,「AIと有機合成化学」第4回公開講演会, Feb, 2020. “化学構造生成器の開発と深層学習を利用した生成モデルについて”
  7. Swarit Jasial, 第6回ケモインフォマティクス秋の学校, Nov, 2019. ”Exploring assay interference characteristics of compounds present in screening data”
  8. 宮尾知幸, 第6回ケモインフォマティクス秋の学校, Nov, 2019. ”Evaluation of different ligand-based virtual screening strategies using 2D/3D molecular representations”
  9. 田村峻佑, 宮尾知幸, 船津公人, 第6回ケモインフォマティクス秋の学校, Nov, 2019. ”Prediction method for active clifffs using machine learning”
  10. 宮尾知幸, 船津公人, 第42回ケモインフォマティクス討論会, Oct, 2019. ”化合物探索のためのiterative screeningにおける機械学習手法の比較”
  11. 青島慎一郎, 宮尾知幸, 船津公人, The 18th Asian Pacific Confederation of Chemical Engineering Congress(APCChE), Sep, 2019. ”Soft sensor models for semi-batch reactors using Gaussian Process”
  12. Tomoyuki Miyao, JCUP X, May, 2019. “Application of turbo similarity searching to molecular shape-based virtual screening”
  13. 田村峻佑, 宮尾知幸, 船津公人, 日本薬学会第139年会, Mar, 2019. ”機械学習によるactivety cliffの予測”
  14. 宮尾知幸, 船津公人, 第41回ケモインフォマティクス討論会, Oct, 2018. “リガンドベースのヴァーチャルスクリーニングにおけるコンフォメーションの影響とアンサンブル効果について”
Copyright©Data-driven Chemistry Group,2022All Rights Reserved.