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.
@article{arxiv.2506.14920,
title = {Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery},
author = {Alejandro Giraldo and Daniel Ruiz and Mariano Caruso and Javier Mancilla and Guido Bellomo},
journal= {arXiv preprint arXiv:2506.14920},
year = {2025}
}