Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917475555213312 |
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| author | Smaldone, Anthony M. Shee, Yu Kyro, Gregory W. Xu, Chuzhi Vu, Nam P. Dutta, Rishab Farag, Marwa H. Galda, Alexey Kumar, Sandeep Kyoseva, Elica Batista, Victor S. |
| author_facet | Smaldone, Anthony M. Shee, Yu Kyro, Gregory W. Xu, Chuzhi Vu, Nam P. Dutta, Rishab Farag, Marwa H. Galda, Alexey Kumar, Sandeep Kyoseva, Elica Batista, Victor S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries Smaldone, Anthony M. Shee, Yu Kyro, Gregory W. Xu, Chuzhi Vu, Nam P. Dutta, Rishab Farag, Marwa H. Galda, Alexey Kumar, Sandeep Kyoseva, Elica Batista, Victor S. Quantum Physics Machine Learning 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. |
| title | Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2409.15645 |