Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries

• URL: http://arxiv.org/abs/2409.15645v1
• Date: Tue, 24 Sep 2024 01:17:34 GMT
• Title: Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Authors: Anthony M. Smaldone, Yu Shee, Gregory W. Kyro, Chuzhi Xu, Nam P. Vu, Rishab Dutta, Marwa H. Farag, Alexey Galda, Sandeep Kumar, Elica Kyoseva, Victor S. Batista,
• Abstract summary: The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery.
• Score: 1.8195318084816288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.

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