Quantum Frontiers in Drug Discovery: Integrating Quantum Computing, Machine Learning, And Quantum Chemistry in Pharmaceutical Research

Abstract:

The pharmaceutical industry faces the dual challenge of rising R&D costs and the increasing complexity of drug discovery processes. Classical computational methods, while successful in many areas, encounter severe scaling limitations when simulating strongly correlated molecular systems. Recent advances in quantum computing including novel quantum algorithms, hybrid quantum-classical pipelines, and quantum machine learning (QML) offer the potential to overcome these limitations by providing exponential improvements in simulation accuracy and speed. This review synthesizes six key studies: a multilayer embedding approach for quantum simulations a perspective on state-of-the-art quantum algorithms for drug discovery, a hybrid quantum computing pipeline for real-world applications the integration of quantum neural networks in drug discovery a comprehensive discussion of quantum pharmacy, and a critical evaluation of quantum mechanics in drug-design workflows We discuss the theoretical underpinnings, compare quantum versus classical approaches, identify current challenges, and forecast future trends. Our analysis suggests that the integration of quantum computing, advanced machine learning, and quantum chemistry will accelerate drug discovery, paving the way toward personalized medicine and more efficient clinical development. Keywords: Pharmaceutical Industry, R&D Costs, Drug Discovery, Computational Methods, Quantum Computing.

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