Quantum Simulation for Drug Discovery: Integrating Computational Science and Pharmaceutical Innovation

Michael Hitt

Quantum Simulation for Drug Discovery: Integrating Computational Science and Pharmaceutical Innovation

Author(s)	Michael Hitt
Country	United States
Abstract	Quantum simulation is emerging as a promising computational paradigm for addressing some of the most complex problems in modern drug discovery. Conventional computational approaches have transformed pharmaceutical research through molecular docking, molecular dynamics, density functional theory, machine learning, and high-throughput virtual screening; however, accurately modelling electronic structure, molecular interactions, reaction pathways, and complex biological environments remains computationally demanding. Quantum computing offers a fundamentally different approach by representing and manipulating quantum-mechanical states directly, potentially enabling more accurate simulations of molecular systems as hardware and algorithms mature. This paper examines the integration of quantum simulation with computational chemistry, artificial intelligence, molecular modelling, and pharmaceutical research. A conceptual qualitative methodology is adopted to analyse quantum algorithms, molecular simulation workflows, hybrid quantum-classical architectures, drug-target modelling, quantum machine learning, and pharmaceutical applications. Particular attention is given to the Variational Quantum Eigensolver, Quantum Phase Estimation, Hamiltonian simulation, quantum-assisted optimisation, and hybrid computational workflows. The study proposes an Integrated Quantum Drug Discovery Framework connecting molecular representation, classical pre-screening, quantum simulation, AI-assisted candidate prioritisation, experimental validation, and iterative optimisation. The analysis indicates that near-term quantum systems are more realistically positioned as complementary tools rather than replacements for established computational chemistry methods. Key barriers include limited quantum hardware, noise, qubit connectivity, error correction requirements, molecular encoding challenges, computational cost, and the difficulty of validating quantum advantage in practical pharmaceutical workflows. The paper concludes that quantum simulation could become an important component of future drug discovery when integrated strategically with high-performance computing, artificial intelligence, quantum chemistry, and experimental science.
Keywords	Quantum Simulation, Quantum Computing, Drug Discovery, Computational Chemistry, Pharmaceutical Innovation, Quantum Chemistry, Molecular Modelling, Quantum Machine Learning, Drug Design, Hybrid Quantum-Classical Computing.
Field	Engineering
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-28

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American Journal of Advanced Multidisciplinary Research and Innovation

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Quantum Simulation for Drug Discovery: Integrating Computational Science and Pharmaceutical Innovation

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