Quantum-Inspired Molecular Docking for Drug Discovery Targeting Biochemical Pathways

Abstract

Molecular docking is a central technique in drug discovery, enabling the prediction of interactions between small molecules and biological targets within complex biochemical pathways. However, classical docking algorithms often face limitations when navigating high-dimensional conformational spaces, accounting for protein flexibility, and identifying optimal binding configurations across large compound libraries. This study explores the application of quantum-inspired molecular docking frameworks as an alternative approach to address these challenges. By drawing on principles such as superposition-inspired parallel search, probabilistic state exploration, and optimization heuristics derived from quantum computing, quantum-inspired models aim to enhance docking accuracy and computational efficiency without requiring fully fault-tolerant quantum hardware. Through qualitative synthesis of research in computational chemistry, drug design, and quantum-inspired optimization, this paper examines how these methods can improve the identification of biologically relevant binding poses and pathway-specific targets. The analysis highlights potential advantages in modeling multi-target interactions, allosteric effects, and pathway-level drug responses. The findings suggest that quantum-inspired docking approaches offer a promising framework for accelerating early-stage drug discovery and for advancing precision medicine by enabling more robust exploration of biochemical interaction networks. Ultimately, this study positions quantum-inspired molecular docking as a scalable and forward-looking strategy for targeting complex biological systems.