Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery

• URL: http://arxiv.org/abs/2506.14920v3
• Date: Wed, 09 Jul 2025 05:09:16 GMT
• Title: Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery
• Authors: Alejandro Giraldo, Daniel Ruiz, Mariano Caruso, Javier Mancilla, Guido Bellomo,
• Abstract summary: This research demonstrates the successful application of a Quantum Multiple Kernel Learning framework to enhance QSAR classification.<n>We apply this methodology to a dataset for identifying DYRK1A kinase inhibitors.<n>By benchmarking the QMKL-SVM against a classical Gradient Boosting model, we show that the quantum-enhanced approach achieves a superior AUC score.
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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