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International Journal of Advanced Research in Science Communication and Technology

Hybrid Quantum-Classical Machine Learning Architectures for Accelerated Drug Discover

Vibhor Pal

Published Jun 30, 2021• 2 citations
Publisher

Abstract

Rapid drug development has been increasingly prevalent over the last ten years, and this trend, when combined with efficient regulatory approvals, has in large part been the driver for the rapid availability of innovative drugs to patients worldwide. This work presents a hybrid Variational Quantum Eigen seeker–Neural Network (VQE-NN) model, a single accelerated drug-solving device that leverages the benefits of both traditional and quantum computers. It performs drug class prediction and feature analysis based on the Kaggle Drug Classification dataset, which contains 2,000–3,000 records with categorical, textual, and numerical features. In data preprocessing, the researchers applied text normalization, tokenization, stemming, and TF-IDF-based feature extraction. The hybrid model presented consists of a VQE module for the optimization of molecular representations and a neural network for classification. In addition, the hyperparameters were carefully tuned to ensure the model learns efficiently and converges. The experimental results show the model's remarkable prediction capability as seen from an accuracy of 97.0%, precision of 96.3%, recall of 96.8%, F1-score of 96.5%, and an AUC of 0.9991. In the performance comparison, the VQE-NN model is claimed to be superior to the existing benchmarks, such as Generative RNN and Seq2Seq Autoencoder models. Besides, the research highlights the benefits of the model, which stem from using quantum entanglement and superposition, which allows for the identification of correlations in higher dimensions and offers a scalable, robust, and state-of-the-art framework for drug prediction

DOI
10.48175/ijarsct-6582m

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Drug repositioning is a less expensive and time-consuming method than the traditional method of drug discovery. It is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. A key strategy in repositioning approved or investigational drugs is determining the binding affinity of these drugs to target proteins. The large increase in available experimental data has helped deep learning methods to demonstrate superior performance compared to conventional prediction and other traditional computational methods in precise binding affinity prediction. However, these methods are complex and time-consuming, presenting a significant barrier to their development and practical application. In this context, quantum computing (QC) and quantum machine learning (QML) theoretically offer promising solutions to effectively address these challenges. In this work, we introduce a hybrid quantum–classical framework to predict binding affinity. Our approach involves, initially, the implementation of an efficient classical model using convolutional neural networks (CNNs) for feature extraction and three fully connected layers for prediction. Subsequently, retaining the classical module for feature extraction, we implement various quantum and classical modules for binding affinity prediction, which accept the concatenated features as input. Quantum predicted modules are implemented with Variational Quantum Regressions (VQRs), while classical predicted modules are implemented with various fully connected layers. Our findings clearly show that hybrid quantum–classical models accelerate the training process in terms of epochs and achieve faster stabilization. Also, these models demonstrate quantum superiority in terms of complexity, accuracy, and generalization, thereby indicating a promising direction for QML.
Abstract Quantum computing has emerged as a powerful tool for modeling molecular systems with high accuracy, accelerating the search for novel therapeutic targets. In this study, we present a two-stage hybrid workflow that combines a Quantum Graph Neural Network with a Variational Quantum Eigensolver to identify potent serine neutralizers in the QM9 dataset. In the first stage, an advanced quantum graph neural network architecture incorporating attention layers, self-distillation, and an adaptive learning-rate schedule is trained to predict ionization potentials and binding free energies. Across five independent random-seed trials, our adaptive-thresholded QGNN–VQE pipeline achieves an average R2R^2R2 R 2 of 0.990±0.0080.990 \pm 0.0080.990±0.008 0.990 ± 0.008 and a mean absolute error of 0.034±0.0010.034 \pm 0.0010.034±0.001 0.034 ± 0.001 eV ( ≈0.79±0.03\approx 0.79 \pm 0.03≈0.79±0.03 ≈ 0.79 ± 0.03 kcal/mol) on the QM9 validation set, demonstrating robust, chemical-accuracy-level predictions. Building on these high-precision predictions, the second stage employs a QAOA-inspired hybrid ranking scheme that merges quantum graph neural network outputs, feature-space similarity (via and cosine similarity), and variational quantum eigensolver-derived energy stability. This α\alpha α α -weighted ( α=0.95\alpha = 0.95α=0.95 α = 0.95 ) scoring framework is applied to over 133,000 molecules, efficiently parsed in parallel. The final ranked list is validated through an improved bijection test, featuring an adaptive similarity threshold to ensure both structural and functional robustness. The top-scoring candidate identified by our pipeline is 5,6,7-tetrahydro-4 H-pyrazolo[4,3-c]pyridin-4-one, highlighting the efficacy of quantum-enhanced modeling in pinpointing complex pharmacological targets. These findings underscore the transformative potential of hybrid quantum-classical workflows in drug discovery, offering a flexible blueprint for advanced screening of other critical biomolecular interactions in the rapidly expanding field of quantum biology. GraphicAbstract VQE for Serine Neutralization
The rapid evolution of drug-resistant HIV strains poses significant challenges for antiretroviral therapy (ART), necessitating advanced computational techniques for drug discovery. Traditional AI-driven molecular docking methods suffer from high computational costs and limited adaptability to mutating HIV variants. To address these limitations, this paper proposes a Hybrid Quantum-AI framework that integrates Variational Quantum Eigensolvers (VQE) with Graph Neural Networks (GNNs) to enhance molecular binding affinity predictions. Experimental validation on real-world HIV mutation datasets demonstrates that the proposed model improves binding accuracy from 75% to 85%, significantly reducing false positives in drug-target interactions. Additionally, the Quantum-AI framework accelerates molecular simulations by 55%, reducing computational time from weeks to hours. The model is implemented using IBM Qiskit and TensorFlow, leveraging quantum variational algorithms for efficient drug screening. Benchmark comparisons indicate an 83% improvement in computational efficiency over traditional AI-based methods, highlighting the feasibility of quantum computing in biomedical applications. These findings suggest that hybrid Quantum-AI models can revolutionize HIV drug discovery, optimize ART regimen personalization, and bridge the gap between computational drug research and clinical applications.
By facilitating quicker and more precise analysis of intricate biological data, the union of quantum computing and Artificial Intelligence (AI) may transform genomic medicine and drug discovery altogether. The study suggests a model for artificial intelligence with quantum-enhanced acceleration based on hybrid quantum-classical models in order to speed up drug discovery and customize therapies. It utilizes deep learning models and Quantum Machine Learning (QML) techniques to analyze patient-specific data, protein structures, and genetic sequences. Large genomic and clinical data sets are processed by conventional deep learning models, while the quantum component carries out efficient data encoding and optimization processes. As compared to other traditional AI-based approaches, the proposed model performs a 30% improved drug target identification when evaluated against more than 1,000 genetic sequences and related clinical outcomes. The hybrid model was as accurate as 98.4% in its prediction and was more accurate than the conventional AI techniques including random forests (accuracy: 91.3%) and gradient boosting machines (accuracy: 93.7%). The methodology also produced an F1-score of 0.95 with accuracy and recall scores of 96.1% and 94.7%, respectively. These results demonstrate how quantum-enhanced AI can reduce the time for computation and improve the accuracy of genomic data analysis, driving significant progress toward personalized therapy and drug development.
The pharmaceutical industry faces mounting challenges in drug discovery, where exploring a chemical space exceeding 10 60 possible compounds remains computationally prohibitive. Traditional in-silico screening approaches struggle to capture the quantum mechanical interactions that determine molecular behavior. To overcome these limitations, this study introduces a hybrid quantum–classical machine learning framework that uniquely integrates molecular graph theory with quantum-enhanced learning for accelerated drug discovery. Using the QM9 benchmark dataset containing 133,885 molecules of low-to-medium structural complexity, we extract Laplacian eigenvalue spectra as topological descriptors that preserve both connectivity and electronic information. These features are jointly processed by Random Forest (RF), Support Vector Machine (SVM), and a Variational Quantum Classifier (VQC) in a weighted ensemble architecture designed for balanced generalization. The Quantum Graph Neural Network (QGNN) component achieves 97.5% accuracy, while the hybrid ensemble attains 100.0% accuracy, confirming strong predictive reliability. Although quantum training requires roughly 3.2× longer computation, it captures high-dimensional correlations that classical methods overlook. Overall, the proposed framework bridges the gap between theoretical quantum computing and practical pharmaceutical modeling, providing a scalable, physics-inspired pathway toward intelligent molecular screening and next-generation drug discovery systems.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.