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This study introduces a quantum molecular docking (QMD) approach using a novel quantum-inspired algorithm. QMD demonstrates superior performance in drug discovery tasks compared to existing methods, highlighting the potential of quantum algorithms.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Quantum computing

Background:

  • Molecular docking (MD) is essential for predicting ligand-protein interactions in drug design.
  • MD can be framed as a combinatorial optimization problem, where quantum annealing (QA) shows promise.
  • Current MD methods face challenges with complex optimization landscapes.

Purpose of the Study:

  • To develop a novel quantum molecular docking (QMD) approach using a quantum annealing-inspired algorithm.
  • To improve the efficiency and accuracy of molecular docking for drug discovery.
  • To demonstrate the applicability of quantum-inspired algorithms to practical drug discovery problems.

Main Methods:

  • Developed two binary encoding methods for efficient discretization of degrees of freedom.
  • Introduced a smoothing filter to rescale the objective function.
  • Proposed a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), for rugged energy landscapes.
  • Incorporated an adaptive local continuous search and a perturbation detection method for pose ranking.

Main Results:

  • The proposed QMD approach, utilizing hSB, demonstrated advantages over Autodock Vina and DIFFDOCK in redocking and self-docking.
  • The binary encoding and smoothing filter efficiently handled the complex objective function.
  • The perturbation detection method improved the stability and ranking of candidate poses.
  • QMD showed significant improvements in accuracy and efficiency.

Conclusions:

  • Quantum-inspired algorithms, like hSB, are effective for solving complex optimization problems in molecular docking.
  • The developed QMD approach offers a promising alternative to existing methods in drug discovery.
  • This work validates the potential of quantum computing applications in pharmaceutical research even with current hardware limitations.