# Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery

arXiv: https://arxiv.org/abs/2506.14920 · HTML: https://mlsanity.com/paper/2506.14920/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)
- [QUACK: Quantum Aligned Centroid Kernel](https://mlsanity.com/paper/2405.00304/) (2024)
- [Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration](https://mlsanity.com/paper/2311.12328/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring](https://mlsanity.com/paper/2510.17688/) (2025)

## Abstract (arXiv)

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.