Quantum-Inspired Machine Learning for Molecular Docking

TL;DR

This paper introduces a quantum-inspired machine learning approach that enhances molecular docking accuracy by combining quantum algorithms with deep learning, outperforming traditional methods and existing deep learning models.

Contribution

The paper presents a novel quantum-inspired algorithm integrated with deep learning for blind molecular docking, achieving higher success rates and better generalization than current methods.

Findings

Outperforms traditional docking algorithms by over 10%.

Improves Top-1 success rate from 33% to 35%.

Achieves 6% better accuracy in high-precision docking on unseen molecules.

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.