Quantum-Inspired Machine Learning for Drug Discovery and Personalized Medicine

Abstract: Quantum‑inspired machine learning (QIML) is gaining attention as a powerful computational paradigm capable of addressing long standing challenges in drug discovery and precision medicine. By borrowing key ideas from quantum mechanics such as superposition, entanglement, and tunneling and implementing them on classical hardware, QIML extends the limits of conventional machine learning. These methods enable more efficient exploration of vast chemical spaces, improved modeling of complex biological systems, and deeper integration of multi omics data. As a result, QIML holds promise for reducing development timelines, lowering costs, and improving prediction accuracy in biomedical research. This review examines the theoretical foundations of QIML, its emerging applications in drug discovery and personalized medicine, associated data and technical challenges, and ethical and regulatory considerations. We also highlight recent case studies and outline future directions that position QIML as a potential cornerstone of next generation healthcare innovation.

Keywords: Quantum Inspired Machine Learning, Drug Discovery, Precision Medicine, Molecular Simulation, Biomarker Identification, Personalized Therapy