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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/2506.14920 |
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| _version_ | 1866911321171165184 |
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| author | Giraldo, Alejandro Ruiz, Daniel Caruso, Mariano Mancilla, Javier Bellomo, Guido |
| author_facet | Giraldo, Alejandro Ruiz, Daniel Caruso, Mariano Mancilla, Javier Bellomo, Guido |
| contents | Quantitative Structure-Activity Relationship (QSAR) modeling is a cornerstone of computational drug discovery. This research demonstrates the successful application of a Quantum Multiple Kernel Learning (QMKL) framework to enhance QSAR classification, showing a notable performance improvement over classical methods. We apply this methodology to a dataset for identifying DYRK1A kinase inhibitors. The workflow involves converting SMILES representations into numerical molecular descriptors, reducing dimensionality via Principal Component Analysis (PCA), and employing a Support Vector Machine (SVM) trained on an optimized combination of multiple quantum and classical kernels. By benchmarking the QMKL-SVM against a classical Gradient Boosting model, we show that the quantum-enhanced approach achieves a superior AUC score, highlighting its potential to provide a quantum advantage in challenging cheminformatics classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery Giraldo, Alejandro Ruiz, Daniel Caruso, Mariano Mancilla, Javier Bellomo, Guido Quantum Physics Machine Learning Quantitative Structure-Activity Relationship (QSAR) modeling is a cornerstone of computational drug discovery. This research demonstrates the successful application of a Quantum Multiple Kernel Learning (QMKL) framework to enhance QSAR classification, showing a notable performance improvement over classical methods. We apply this methodology to a dataset for identifying DYRK1A kinase inhibitors. The workflow involves converting SMILES representations into numerical molecular descriptors, reducing dimensionality via Principal Component Analysis (PCA), and employing a Support Vector Machine (SVM) trained on an optimized combination of multiple quantum and classical kernels. By benchmarking the QMKL-SVM against a classical Gradient Boosting model, we show that the quantum-enhanced approach achieves a superior AUC score, highlighting its potential to provide a quantum advantage in challenging cheminformatics classification tasks. |
| title | Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2506.14920 |