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.
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.
Accurate prediction of binding affinities and efficient lead optimization remain central challenges in computational drug discovery, particularly for complex biological targets characterized by rugged energy landscapes, conformational heterogeneity, and high-dimensional interaction spaces. Classical computational methods—such as molecular docking, force-field–based simulations, and deep learning models—have made substantial progress, yet they often struggle to capture quantum-mechanical effects and long-range correlations critical to molecular recognition. This study explores a quantum-enhanced framework that integrates Variational Quantum Algorithms (VQAs), quantum-inspired Hamiltonian modeling, and hybrid quantum–classical optimization workflows to improve the fidelity and scalability of drug discovery pipelines. By encoding molecular interactions into parameterized quantum circuits, the framework leverages quantum superposition and entanglement to explore complex chemical space more efficiently, enabling refined estimation of binding energies and accelerated identification of high-quality leads. Preliminary simulations indicate that VQA-based estimators outperform classical baselines for challenging protein–ligand systems, reducing prediction error while maintaining computational tractability. These findings highlight the emerging potential of quantum technologies to enable more accurate, data-efficient, and mechanistically grounded drug discovery for next-generation therapeutics.
Quantum Machine Learning (QML) is a newly emerging research area at the intersection of classical machine learning (CML) and quantum computing (QC). Data is becoming voluminous rapidly, so it is challenging for classical computers to train Machine Learning (ML) models over massive datasets. The hope is that quantum physics' intrinsic features, such as entanglement, superposition, and interference, could be exploited as resources for training ML models on big datasets that would otherwise be relatively impossible for classical computers. It is theoretically proven that quantum computers have an exponential-time advantage over their classical counterparts in solving several problems, e.g., complex large dimensional matrix multiplication, factorization problem, unstructured database search, etc. QML models attempt to find a quantum advantage over their classical counterparts. Variational Quantum Classifiers (VQC) are hybrid quantum neural networks to perform the task of classification using QML models. VQC models in the present NISQ (Noisy Intermediate Scale Quantum — 50 to 100 qubits) era can produce comparable and even better results than Classical models. In this article, we examined the performance of a VQC while performing a simple binary classification task. In this article, we use a VQC to evaluate this method's performance empirically. We constructed a VQC to predict the label of fresh input for the typical Iris dataset comprising pairings of target outputs and training inputs. Our quantum classifier can reasonably predict species labels using only four qubits. These levels are fairly good compared to the accuracy levels attained by classical classifiers.
Quantum machine learning (QML) has been demonstrated to have great potential in revolutionizing the process of drug discovery by dealing with the problem of high computational complexity of molecular simulations. Conventional computational techniques are not very effective in predicting the properties of molecules and pharmaceutical activity because the simulation of large molecules is too complex. This paper will discuss how QML algorithms can be used, specifically variational quantum circuits and quantum kernel methods, to improve the predictability of molecular properties and accelerate the process of drug discovery. To enhance the prediction accuracy, the research use quantum-enhanced feature extraction together with classical machine learning technologies. Research methodology is applied to datasets of molecular structure and biological activity in order to provide a more useful model to learn about how molecular features relate to biological outcomes using quantum machine learning models. These results suggest that it is 15 % more accurate in prediction and 20 % faster in computing high-dimensional molecular simulations compared to standard machine learning algorithms. The efficiency of the QML-based method is statistically proven with an accuracy 89%, precision 87%, and an F1-score of 0.91. The results point to the possible ability of QML to decrease the time and computational resources spent on the exploration of drug discovery, providing a new direction for the improvement of molecular property prediction. The study is a contribution to the emerging area of QML in pharmaceutical use and demonstrates that it can simplify drug discovery and enhance the accuracy with which predictions are made. The paper demonstrates that QML may produce a significant impact on the drug development process by simulating the molecular properties faster, more effectively, and more accurately.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
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.
The large search space and the challenge of accurately simulating quantum-level molecular interactions are the fundamental causes of the delayed, complicated, and costly process of discovering new medications. The effectiveness of conventional computational techniques, including molecular docking and molecular dynamics, to assess molecular characteristics and provide highly accurate drug candidate predictions is constrained. This paper presents a Quantum-Assisted Drug Discovery Simulation framework that combines cutting-edge quantum computing algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE), with traditional molecular docking tools like RDKit and AutoDock. The system uses these quantum algorithms to perform very accurate Quantum Refinement on the most potential therapeutic protein poses found by classical identification. This hybrid approach seeks to optimize drug-protein interactions more quickly, speed up early-stage drug discovery, and create opportunities for less costly, more accurate medical research, even though a combination of quantum and classical calculations allows more accurate prediction of molecular energies and binding affinities.