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arXiv · 2409.15645

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

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
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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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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. 2024-09-24. Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries. https://doi.org/10.1021/acs.chemrev.4c00678

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