Development of Machine Learning Models withFragment Molecular Orbital Calculation Data

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  • KATO Koichiro
    Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan Center for Molecular Systems, Graduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
  • MATSUMOTO Hiromu
    Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
  • KITA Ryosuke
    Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

Bibliographic Information

Other Title
  • フラグメント分子軌道法で生成したデータを用いた機械学習モデルの開発
Published
2024
DOI
  • 10.2477/jccj.2024-0015
Publisher
Society of Computer Chemistry, Japan

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<p>Abstract: Fragment Molecular Orbital (FMO) is a unique method that allows quantum mechanical (QM) calculations of entire proteins. The data obtained by the FMO method are also currently the only QM calculation data for protein systems. The development of various machine learning models using the QM calculation data of proteins, which are difficult to generate with general-purpose software, is expected to have a significant impact on AI drug discovery, which has been remarkably active in recent years. This paper outlines the status of the development of machine learning models using FMO data, which is ongoing in the author's group.</p>

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