化学×AI技術で世界を変える研究室

Publication

Publication

Research articles

2024

  1. Zhang F., Miyao T., Izumiya Y., et al., “Designing Heat-Resistant and Moldable Polyester Resin by the Integration of Machine Learning Models with Expert Knowledge”, ACS Appl. Polym. Mater., 2024
  2. Shirasawa R., Takaki K., Miyao T., “Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships”, ACS Omega, 9, 8, 9463-9474, 2024

2023

  1. Takasuka S., Oikawa S., Yoshimura T., et al., “Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers”, Digital Discovery, 2, 3, 809-818, 2023
  2. Matsunaga K., Harada T., Harada S., Sato A., et al., “Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process”, ACS Omega, 8, 30, 27458-27466, 2023
  3. Wakiuchi A., Jasial S., Asano S., et al., “Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra”, ACS Omega, 2023
  4. Wakiuchi A., Takasuka S., Asano S., “Composition Regulation by Flow Copolymerization of Methyl Methacrylate and Glycidyl Methacrylate with Free Radical Method”, Macromol. Mater. Eng., 2200626, 2023
  5. Tamura S., Miyao T., Bajorath J., “Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity”, J. Cheminform., 15, 4, 2023

2022

  1. Jasial S., Hu J., Miyao T., Hirama Y., Onishi S., Matsui R., Osaki K., Funatsu K., “Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models”, ASC Pharmacol. Transl. Sci., 2022.
  2. Takaki K., Miyao T., “Symbolic Regression for the Interpretation of Quantitative Structure-Property Relationships”, Artificial Intelligence in the Life Sciences, 100046, 2022.
  3. Muhammad A., Louis F., Miyao T. Lee S. H., Chang Y. T., Matsusaki M., “”Mechanism assay of interaction between blood vessels-near infrared probe and cell surface marker proteins of endothelial cells”, Materials Today Bio, 15, 100332, 2022.
  4. Asahara R., Miyao T., “Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction”, ACS Omega, 7, 30, 26952-26964, 2022.
  5. Nakano H., Miyao T., “Visualization of Topological Pharmacophore Space with Graph Edit Distance”, ACS Omega, 7, 16, 14057-14068, 2022.
  6. 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.
  7. 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.
  8. 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. 浅原崚佑, 第45回ケモインフォマティクス討論会, Nov, 2022, 優秀講演賞
  2. 船津公人, 日本化学会, Jan, 2021, 第38回学術賞
  3. 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
  4. 青島慎一郎, The 18th Asian Pacific Confederation of Chemical Engineering Congress(APCChE), Sep, 2019, Excellent Poster Award
  5. 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

2023

  1. Akinori Sato, Tomoyuki Miyao, The 2nd International Symposium on Digitalization-driven Transformative Organic Synthesis, Dec, 2023, “Prediction of Reaction Yield for High Throughput Experimental Data Sets by Deep Learning”
  2. Swarit Jasial, 第8回ケモインフォマティクス秋の学校, Nov, 2023. ”Prediction of Monomer Concentrations in Copolymerization Reactions from Infrared (IR) Spectra”
  3. Akinori Sato, Tomoyuki Miyao, 第8回ケモインフォマティクス秋の学校, Nov, 2023. ”Prediction of Reaction Yield for High Throughput Experimental Data Sets by Deep Learning”
  4. Rezi Riadhi, Tomoyuki Miyao, 第8回ケモインフォマティクス秋の学校, Nov, 2023. “Utilization of Hybrid Fragmentation Fingerprints in SARMs Dataset”
  5. 松永幹太, 上沼睦典, 佐藤彰准, 浦岡行治, 宮尾知幸, 化学工学会第54回秋季大会, Sep, 2023, “プロセス最適化のためのガウス過程回帰手法:絶縁体と半導体間の界面準位密度予測の事例”
  6. 宇惠崇人, 宮尾知幸, 第46回ケモインフォマティクス討論会, Nov, 2023, “化合物の類縁体合成可能性スコアの開発”
  7. 宮尾 知幸, 日本薬学会第143年会, Mar, 2023. “機械学習による低分子スクリーニング手法の開発”
  8. Rezi Riadhi, 宮尾 知幸, 日本薬学会第143年会, Mar, 2023. “Utilization of interaction fingerprints to compare performance with ligand-based virtual screening approaches.”
  9. 前田 樹, 宮尾 知幸, 日本薬学会第143年会, Mar, 2023, “活性予測のための不均衡データにおけるアンダーサンプリング手法の比較

2022

  1. Tomoyuki Miyao, 第7回ケモインフォマティクス秋の学校, Nov, 2022. ”Global Interpretation of Regression Models for Quantitative Structure-Property Relationship”
  2. Swarit Jasial, 第7回ケモインフォマティクス秋の学校, Nov, 2022. ”Understanding Feature Interpretations of Machine Learning/Regression Models”
  3. Tomoyuki Miyao, The 6th International Conference on Advance Pharmacy and Pharmaceutical Sciences (ICAPPS), Oct, 2022. “Role of Cheminformatics in Pharmaceutical Research”
  4. Swarit Jasial, Virtual Summer Course by Faculty of Pharmacy, University of Indonesia, Aug, 2022, “Chemoinformatics: In Silico Techiniques Applied to a Range of Problems in Chemistry”
  5. 浅原崚佑, 宮尾知幸, 第45回ケモインフォマティクス討論会, Nov, 2022, “リン酸不斉触媒の選択性予測モデルのための分子表現と解釈”
  6. 佐藤彰准, 宮尾知幸, 第45回ケモインフォマティクス討論会, Nov, 2022, “深層学習を用いた化学反応の収率予測モデルの構築”
  7. 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”

2021

  1. 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”
  2. 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”

2020

  1. 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”
  2. 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”
  3. 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”

2019

  1. 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”
  2. 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”
  3. 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”
  4. 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”
  5. 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”
  6. Tomoyuki Miyao, JCUP X, May, 2019. “Application of turbo similarity searching to molecular shape-based virtual screening”
  7. 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”

2018

  1. 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”

著書

  1. 宮尾知幸, “ケモインフォマティクスにおけるデータ収集の最適化と解析手法　～組成予測や化学構造の生成、合成経路探索や反応条件最適化、毒性評価～ 1章1節”, 2023
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