The use of principles of quantum mechanical with machine learning algorithms is a paradigm shift in structure-based drug design, solving fundamental limitations in conventional molecular modeling methods. Quantum Machine Learning (QML) have shown better accuracy in predicting protein-ligand binding affinities by incorporating quantum effects traditionally overlooked in classical force fields. The quantum-classical hybrid methodology consists of quantum mechanical calculations for critical molecular regions while utilizing classical mechanics for the broader protein environment, optimizing computational efficiency without compromising accuracy. Deep learning architectures, specifically quantum neural networks (QNNs), have transformed the molecular visualization through quantum tensor networks, enabling the capture of complex electronic interactions and conformational dynamics. These advanced techniques have shown particular efficacy in handling challenging cases such as metalloproteins, where traditional force fields often fail due to inadequate treatment of d-orbital electrons and complex coordination geometries. The incorporation of quantum mechanical effects has significantly improved the treatment of polarization phenomena, especially in predicting binding affinities where electronic redistribution plays a crucial role. Recent developments in quantum-inspired algorithms have improved conformational sampling efficiency, providing more thorough exploration of protein-ligand binding landscapes. The treatment of water molecules in binding sites, historically a significant challenge in molecular docking, has been refined through quantum mechanical descriptions of hydrogen bonding networks and water-mediated interactions. While these trends mark significant progress, current limitations include computational scalability, particularly for large protein-ligand systems, quantum decoherence in hybrid calculations, and the need for more extensive experimental validation datasets. The convergence of quantum computing capabilities with sophisticated machine learning algorithms continues to expand the horizons of structure-based drug design, promising more accurate and efficient drug discovery processes
Keywords: Quantum machine learning, Structure-based drug design, Quantum-classical hybrid methods, Protein-ligand interactions, Deep learning architectures
How to cite this article: Udayakumar N, Kumar PN, Meghana G, Sudharsan P, Kavitha P, Munikeerthana V, Naga Pallavi V, Quantum Machine Learning for Predicting Molecular Interaction and Structure-Based Drug Design - A Review. Int J Drug Deliv Technol. 2026;16(1): 59-71. DOI: 10.25258/ijddt.16.1.7