Quantum-Inspired Machine Learning for Molecular Docking

• URL: http://arxiv.org/abs/2401.12999v2
• Date: Thu, 22 Feb 2024 02:56:06 GMT
• Title: Quantum-Inspired Machine Learning for Molecular Docking
• Authors: Runqiu Shu, Bowen Liu, Zhaoping Xiong, Xiaopeng Cui, Yunting Li, Wei Cui, Man-Hong Yung and Nan Qiao
• Abstract summary: Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. We introduce quantum-inspired algorithms combining quantum properties and spatial optimization problems. Our method outperforms traditional docking algorithms and deep learning-based algorithms over 10%.
• Score: 9.16729372551085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial range. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. Quantum-inspired algorithms combining quantum properties and annealing show great advantages in solving combinatorial optimization problems. Inspired by this, we achieve an improved in blind docking by using quantum-inspired combined with gradients learned by deep learning in the encoded molecular space. Numerical simulation shows that our method outperforms traditional docking algorithms and deep learning-based algorithms over 10\%. Compared to the current state-of-the-art deep learning-based docking algorithm DiffDock, 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 the high-precision region(RMSD<1) on molecules data unseen in DiffDock, which demonstrates the well-generalized of our method.

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