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    Encoding Molecular Docking for Quantum Computers
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    • Jinyin Zha
      Jinyin Zha
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
      Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
      More by Jinyin Zha
    • Jiaqi Su
      Jiaqi Su
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
      More by Jiaqi Su
    • Tiange Li
      Tiange Li
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
      More by Tiange Li
    • Chongyu Cao
      Chongyu Cao
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
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    • Yin Ma
      Yin Ma
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
      More by Yin Ma
    • Hai Wei
      Hai Wei
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
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    • Zhiguo Huang
      Zhiguo Huang
      China Mobile (Suzhou) Software Technology Company Limited, Suzhou 215163, China
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    • Ling Qian
      Ling Qian
      China Mobile (Suzhou) Software Technology Company Limited, Suzhou 215163, China
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    • Kai Wen*
      Kai Wen
      Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China
      *Email: wenk@boseq com Tel: +86-10-53392661 Fax: +86-10-53392661.
      More by Kai Wen
    • Jian Zhang*
      Jian Zhang
      Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
      *Email: [email protected]. Tel: +86-21-63846590. Fax: +86-21-64154900
      More by Jian Zhang
      Orcidhttps://orcid.org/0000-0002-6558-791X
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    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2023, 19, 24, 9018–9024
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    https://doi.org/10.1021/acs.jctc.3c00943
    Published December 13, 2023
    Copyright © 2023 American Chemical Society
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    Abstract

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    Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.

    Copyright © 2023 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.3c00943.

    • Section S1 is a detailed description of solving QUBO Models with QCs. Section S2 explains why only van der Waals term is included in GPM. Section S3 introduces the AutoSite algorithm. Section S4 evaluates the docking power pf GPM and FAM. Section S5 introduces procedures of data collection. Section S6 introduces the parametrization procedures and results. Section S7 discusses the sensitivity of GPM and FAM. Table S1 lists the training set. Table S2 lists the test set. Table S3 lists the performance of Feature Atom Matching in 10 runs. (PDF)

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    This article is cited by 6 publications.

    1. Yujie You, Kan Tan, Zekun Jiang, Le Zhang. Developing a Predictive Platform for Salmonella Antimicrobial Resistance Based on a Large Language Model and Quantum Computing. Engineering 2025, 85 https://doi.org/10.1016/j.eng.2025.01.013
    2. Yinuo Wan, Guangyuan Tian, Haikuo Xi, Yong Fang. Optimization Method for Microgrid Demand Response Based on Coherent Photonic Quantum Computing. 2024, 143-147. https://doi.org/10.1109/ICPES63746.2024.10856580
    3. Yuan Gao, Guanyu Chen, Luo Qi, Wujie Fu, Zifeng Yuan, Aaron J. Danner. Photonic Ising machines for combinatorial optimization problems. Applied Physics Reviews 2024, 11 (4) https://doi.org/10.1063/5.0216656
    4. Yanbo (Justin) Wang, Xuan Yang, Chao Ju, Yue Zhang, Jun Zhang, Qi Xu, Yiduo Wang, Xinkai Gao, Xiaofeng Cao, Yin Ma, Jie Wu. Quantum Computing in Community Detection for Anti-Fraud Applications. Entropy 2024, 26 (12) , 1026. https://doi.org/10.3390/e26121026
    5. Giacomo Lancellotti, Gianmarco Accordi, Gianluca Palermo. An Experimental Approach to Quantum Molecular Docking. 2024, 512-518. https://doi.org/10.1109/QCE60285.2024.00066
    6. Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama. QUBO Problem Formulation of Fragment-Based Protein–Ligand Flexible Docking. Entropy 2024, 26 (5) , 397. https://doi.org/10.3390/e26050397
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    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2023, 19, 24, 9018–9024
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.3c00943
    Published December 13, 2023
    Copyright © 2023 American Chemical Society
    Request reuse permissions

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