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arXiv · 2506.14920

Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery

Alejandro Giraldo·Daniel Ruiz·Mariano Caruso·Javier Mancilla·Guido Bellomo
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Abstract

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.

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Alejandro Giraldo, Daniel Ruiz, Mariano Caruso, Javier Mancilla, Guido Bellomo. 2025-06-17. Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery. https://arxiv.org/abs/2506.14920

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