Introduction: Histone demethylase inhibitors (HDMIs) target histone demethylase enzymes, which play a key role in regulating gene expression. Quantum machine learning, particularly Variational Quantum Classifiers (VQCs), offers potential advantages by enabling efficient analysis of large-scale genomic data. Objectives: This study investigates the use of VQCs to predict and classify protein targets in drug-gene interactions involving HDMIs. VQCs can reveal subtle patterns, manage the complexity of genomic datasets, and deliver highly accurate predictions, surpassing the capabilities of traditional machine learning approaches. Methods: Drug-gene interactions within the HDMI signaling pathways were compiled from a dataset of 28,280 drug-gene pairs. A subset of 4,000 protein interactions was selected for quantum machine learning analysis. The Variational Quantum Classifier was employed, integrating classical and quantum components to transform the data into a higher-dimensional space, with iterative optimization of the quantum circuit. Results:The VQC achieved a classification accuracy of 97.88% in predicting drug-gene interactions. However, the model's ranking performance was moderate, possibly due to the complexity of the task or the chosen feature mapping approach. On a test dataset, the model demonstrated an accuracy of 97.68%, with 100% recall and high precision. Despite these results, a relatively low F1 score indicates potential performance discrepancies across different classes. Conclusions: The Variational Quantum Classifier shows strong potential in predicting protein targets involved in drug-gene interactions for histone demethylase inhibitors, contributing to advancements in precision medicine for Oro-digestive cancers. Nevertheless, further refinement and optimization of the model are needed to enhance its overall performance and robustness.
Introduction: Histone demethylase inhibitors (HDMIs) target histone demethylase enzymes, which play a key role in regulating gene expression. Quantum machine learning, particularly Variational Quantum Classifiers (VQCs), offers potential advantages by enabling efficient analysis of large-scale genomic data. Objectives: This study investigates the use of VQCs to predict and classify protein targets in drug-gene interactions involving HDMIs. VQCs can reveal subtle patterns, manage the complexity of genomic datasets, and deliver highly accurate predictions, surpassing the capabilities of traditional machine learning approaches. Methods: Drug-gene interactions within the HDMI signaling pathways were compiled from a dataset of 28,280 drug-gene pairs. A subset of 4,000 protein interactions was selected for quantum machine learning analysis. The Variational Quantum Classifier was employed, integrating classical and quantum components to transform the data into a higher-dimensional space, with iterative optimization of the quantum circuit. Results:The VQC achieved a classification accuracy of 97.88% in predicting drug-gene interactions. However, the model's ranking performance was moderate, possibly due to the complexity of the task or the chosen feature mapping approach. On a test dataset, the model demonstrated an accuracy of 97.68%, with 100% recall and high precision. Despite these results, a relatively low F1 score indicates potential performance discrepancies across different classes. Conclusions: The Variational Quantum Classifier shows strong potential in predicting protein targets involved in drug-gene interactions for histone demethylase inhibitors, contributing to advancements in precision medicine for Oro-digestive cancers. Nevertheless, further refinement and optimization of the model are needed to enhance its overall performance and robustness.
Abstract Understanding and predicting cancer phenotypes from genomic data requires models capableof capturing high-order interactions among genes and biological pathways. We introduce Quantum Interaction Manifolds (QIMs), a theoretical framework that represents gene expression profiles as quantumstates evolved under biologically informed Hamiltonians. We show that QIM embeddings induce hypothesisclasses that cannot be efficiently approximated by classical learners under standard complexity assumptions,providing a principled quantum advantage in representing nonlinear genomic dependencies. Using noisysimulations on reduced-dimensional datasets, we demonstrate that QIM-based quantum kernels achievecompetitive accuracy in cancer classification tasks, while variational quantum models trained on QIMrepresentations learn continuous drug response signals with stable convergence. Together, these results establish QIMs as a quantum-native approach for modeling genomic interactions and highlight the potentialof structured quantum machine learning to support future developments in computational oncology
The need to discover therapeutic compounds has intensified in recent years due to the rise of antibiotic resistance and complexity of modern diseases. The conventional drug discovery pipeline is resource-intensive. The field of drug development has evolved into interdisciplinary science. Several computational techniques have been devised to assist researchers, in the identification of new drugs. These tools often employ machine learning algorithms that are typically executed on classical computing machines. However, recent advancements in the field of quantum computing have offered new possibilities to improve machine learning models. Quantum computing leverages unique quantum-mechanical characteristics to achieve more efficient computations. In this paper, we introduce a framework that utilizes Variational Quantum Classifier (VQC) techniques for the classification of drug-protein interactions, as shown in Figure 1. This approach exploit quantum computing to produce better results than classical computing methods. To validate the efficacy of our proposed framework, we utilized the KIBA dataset to assess the drug-target affinity score. The model was trained at a learning rate of 0.0001 over 500. Through a 3-fold cross-validation, we achieved a concordance index (CI) of 0.802. This performance surpasses that of linear regression technique (kronLRS), which has a concordance index of 0.68 and is comparable to deep learning methods, such as DeepCPI and DeepDTA, which have a CI of 0.86. Notably, standard VQC methods typically attain a CI of approximately 0.75, which is a metric that can be further optimized by adjusting the number of qubits or wires in the circuit.
Early and accurate diagnosis of breast cancer plays a critical role in improving patient survival rates and reducing treatment costs. Classical machine learning algorithms such as Logistic Regression and Support Vector Machines (SVM) have demonstrated strong performance in medical diagnosis tasks; however, their scalability and representational limits motivate the exploration of emerging paradigms. Quantum Machine Learning (QML), particularly Variational Quantum Classifiers (VQC), has recently gained attention due to its potential to model complex decision boundaries using quantum circuits. This paper presents a comparative analysis between a Variational Quantum Classifier and classical machine learning models for binary breast cancer classification. The study employs dimensionality-reduced clinical data and evaluates model performance using accuracy, confusion matrices, decision boundary visualization, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) curves. Experimental results indicate that the VQC achieves competitive performance with classical approaches, demonstrating its feasibility for small-scale medical datasets and highlighting the promise of quantum-enhanced learning in healthcare applications.
In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. MM, an extremely heterogeneous condition, displays often mixed responses to treatment, with a large number of patients showing drug resistance to proteasome inhibitors and immune modulatory agents. However, the methods previously used for genomic and proteomic data analysis techniques are plagued by issues of high dimensionality, imbalanced class distribution and feature redundancy, which work against the accurate predictability and generalizability of such methods. These are compounded by the so-called "curse of dimensionality", with dimensions far outnumbering samples, hence classical model overfitting. In this work, we present QProteoML as an integration of quantum techniques purposefully developed to deal with high-dimensional, imbalanced and redundant data. The framework integrates a combination of Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (qPCA), Quantum Annealing (QA) for feature selection and Quantum Generative Adversarial Networks (QGANs) for data augmentation. These quantum algorithms exploit certain quantum phenomena (superposition and entanglement) to perform modelling of nonlinear relationships, dimensionality reduction, and class-imbalance issues. QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. In addition, Quantum Annealing successfully extracts the most informative biomarkers with low redundancy. QProteoML was experimentally tested by comparing accuracy, F1 score and AUC ROC between classical machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). Our results demonstrate that QProteoML performs better than classical models, particularly in identifying the drug resistant minority class of patients. Additionally, the model is interpretable and stresses important biomarkers of drug sensitivity in MM. This research opens the possibility of quantum machine learning in personalised medicine for Multiple Myeloma. It demonstrates that quantum algorithms can perform complex biological data suggesting more reliable and accurate drug sensitivity predictions. Future research will be directed toward clinical validation of the given system with larger and more diverse cohorts of MM patients; the integration of quantum hardware for practical applications.
Multi-omics integration has become a cornerstone of personalized medicine, enabling researchers to connect genomic, transcriptomic, proteomic, metabolomic, and epigenomic data to reveal complex biological mechanisms and individualized disease signatures. However, the extreme dimensionality, heterogeneity, and nonlinear interactions across omics layers challenge even the most advanced classical machine learning methods. Quantum Machine Learning (QML) offers a powerful alternative by exploiting quantum superposition, entanglement, and high-dimensional feature embeddings to model multi-omics interactions more efficiently. This study proposes a hybrid quantum–classical framework that combines quantum kernel estimation, variational quantum classifiers, and quantum-enhanced feature fusion networks to generate integrative biological signatures for precision diagnostics. Simulation results using benchmark multi-omics datasets demonstrate improved classification accuracy, enhanced sensitivity to weak biological signals, and reduced computational overhead compared to classical baselines. These findings highlight the potential of QML to accelerate multi-omics insights and pave the way toward truly personalized, predictive healthcare.
Quantum computing offers new opportunities for addressing complex classification tasks in biomedical applications. This study investigates two quantum machine learning models-the Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC)-in the context of B-cell epitope prediction, a key step in modern vaccine design. QSVM builds upon the classical SVM framework by using quantum circuits to encode nonlinear kernel computations, while VQC replaces the entire classification pipeline with trainable quantum circuits optimized variationally. A benchmark dataset from the Immune Epitope Database (IEDB) is used for model evaluation. Each epitope is represented by 10 physicochemical features, and dimensionality reduction via Principal Component Analysis (PCA) is applied to assess model performance across different feature spaces. We also examine the effect of sample size on prediction outcomes. Experimental results show that QSVM performs well under limited data conditions, while VQC achieves higher accuracy in larger datasets. These findings highlight the potential of quantum-enhanced models for bioinformatics tasks, particularly in supporting efficient and scalable epitope-based vaccine development.