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.

Related papers

• TensoMeta-VQC: A Tensor-Train-Guided Meta-Learning Framework for Robust and Scalable Variational Quantum Computing [60.996803677584424]
  TensoMeta-VQC is a novel tensor-train (TT)-guided meta-learning framework designed to improve the robustness and scalability of VQC significantly.<n>Our framework fully delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering [41.94295877935867]
  We implement both quantum and classical solvers to compare their effectiveness.<n>For feature selection, we propose several QUBO configurations that balance feature importance and redundancy.<n>In instance selection, we propose a few novels for instance-level importance measures that extend existing methods.<n>For clustering, we embed a classical-to-quantum pipeline, using classical clustering followed by QUBO-based medoid refinement.
  arXiv  Detail & Related papers  (2025-07-20T17:59:14Z)
• Quantum QSAR for drug discovery [41.94295877935867]
  Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery.<n>This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs)<n>By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
  arXiv  Detail & Related papers  (2025-05-06T17:58:33Z)
• Quantum Phases Classification Using Quantum Machine Learning with SHAP-Driven Feature Selection [0.0]
  We present an innovative methodology to classify quantum phases within the ANNNI (Axial Next-Nearest Neighbor Ising) model.<n>Our investigation focuses on two prominent QML algorithms: Quantum Support Vector (QSVM) and Variational Quantums (VQC)<n>The results reveal that both QSVM and VQC exhibit exceptional predictive accuracy when limited to 5 or 6 key features.
  arXiv  Detail & Related papers  (2025-04-14T19:51:26Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation [41.01256771536732]
  This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation. Our results indicate that quantum-inspired and quantum methods offer a promising alternative for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.
  arXiv  Detail & Related papers  (2024-10-14T16:51:59Z)
• Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification [0.0]
  We present a quantum machine learning model for the diagnosis of Progressive Supranuclear Palsy (PSP) disorder. The results suggest that quantum machine learning has led to noticeable advancement and outperforms classical frameworks. In particular, we have demonstrated the successful application of the present model on both a quantum simulator and real chips of the IBM quantum platform.
  arXiv  Detail & Related papers  (2024-07-06T14:16:31Z)
• Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines [7.106829260811707]
  We present an approach to employ Quantum Annealing (QA) in image classification. We propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score.
  arXiv  Detail & Related papers  (2023-11-27T16:07:49Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage [0.0]
  We show that a quantum integrated workflow can provide a tangible advantage compared to state-of-art classical algorithms. We also test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.
  arXiv  Detail & Related papers  (2022-04-08T12:05:27Z)
• Reducing Unitary Coupled Cluster Circuit Depth by Classical Stochastic Amplitude Pre-Screening [0.0]
  Unitary Coupled Cluster (UCC) approaches are an appealing route to utilising quantum hardware to perform quantum chemistry calculations. We present a combined classical-quantum approach where a classical UCC pre-processing step is used to determine the important excitations in the UCC ansatz.
  arXiv  Detail & Related papers  (2021-08-24T18:34:14Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.