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Molecular docking is an important tool for structure-based drug design,accelerating the efficiency of drug development. Complex and dynamic bindingprocesses between proteins and small molecules require searching and samplingover a wide spatial range. Traditional docking by searching for possiblebinding sites and conformations is computationally complex and results poorlyunder blind docking. Quantum-inspired algorithms combining quantum propertiesand annealing show great advantages in solving combinatorial optimizationproblems. Inspired by this, we achieve an improved in blind docking by usingquantum-inspired combined with gradients learned by deep learning in theencoded molecular space. Numerical simulation shows that our method outperformstraditional docking algorithms and deep learning-based algorithms over 10\%.Compared to the current state-of-the-art deep learning-based docking algorithmDiffDock, the success rate of Top-1 (RMSD<2) achieves an improvement from 33\%to 35\% in our same setup. In particular, a 6\% improvement is realized in thehigh-precision region(RMSD<1) on molecules data unseen in DiffDock, whichdemonstrates the well-generalized of our method.

Quantum-Inspired Machine Learning for Molecular Docking