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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.04648 |
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| _version_ | 1866908439455727616 |
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| author | Giraldo, Alejandro Ruiz, Daniel Caruso, Mariano Bellomo, Guido |
| author_facet | Giraldo, Alejandro Ruiz, Daniel Caruso, Mariano Bellomo, Guido |
| contents | Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04648 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum QSAR for drug discovery Giraldo, Alejandro Ruiz, Daniel Caruso, Mariano Bellomo, Guido Quantum Physics Machine Learning Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models. |
| title | Quantum QSAR for drug discovery |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2505.04648 |