A machine learning model for predicting quantum chemistry based protein-drug molecule interactions

DOI

説明

<p>The evaluation of protein-drug molecule interactions through molecular simulation plays a critical role in identifying drug candidates from a vast pool of chemical compounds in computational drug discovery. To increase the hit rate, which has been a challenge with traditional methods, accurate quantum chemical calculations of protein-drug molecule interactions are necessary. However, evaluating protein-drug molecule interactions using conventional quantum chemistry calculation methods is challenging. The Fragment Molecular Orbital (FMO) method allows for the calculation of protein-drug molecule interactions with quantum chemistry accuracy. Still, even using the "Fugaku" supercomputer, it takes several hours per structure, indicating a need for further reduction in computational costs. This study presents a machine learning model that predicts interaction values between proteins and drug molecules using the FMO method. This model is based on a neural network and utilizes vectors of the surrounding environment of each atom in the drug molecule as explanatory variables. Using a dataset of approximately 2000 structures, the model was trained and tested for predicting interactions in unknown structures. The model successfully predicted protein-drug molecule interactions with an R2 value of 0.59.</p>

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詳細情報 詳細情報について

  • CRID
    1390863395972634752
  • DOI
    10.11517/pjsai.jsai2024.0_4q3is2d05
  • ISSN
    27587347
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
  • 抄録ライセンスフラグ
    使用不可

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