The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein in the Receptor Tyrosine Kinases (RTKs) family, playing a central role in cell growth, motility, and differentiation. Mutations in EGFR are associated with various cancers, notably Non-Small Cell Lung Cancer (NSCLC), due to overexpression and dysregulated signaling. To support early-stage drug discovery targeting EGFR, this work introduces Q-CaDD (Quantum-enhanced Computer-aided Drug Design), a hybrid quantum–classical computational framework. Q-CaDD integrates quantum machine learning (QML), classical models, molecular docking, and multi-stage ligand filtering. Over 200,000 ligands were generated using STONED-SELFIES and filtered using Ghose and Quantitative Estimate of Drug-Likeness (QED) criteria. Docking simulations were performed using AutoDock Vina, and toxicity prediction employed an ensemble of machine learning models including a Quantum Support Vector Machine (QSVM). Model evaluation was conducted on the Tox21 dataset using the NR-AR (Androgen Receptor) pathway as a proof-of-concept toxicity endpoint. The hybrid ensemble achieved an AUC-ROC of ~ 0.76, demonstrating improved generalisation relative to standalone classical models under the same evaluation setting and competitive performance with respect to reported benchmark results. Several compounds exhibited favourable predicted affinity and toxicity profiles and are identified as preliminary candidate molecules pending further experimental validation. Overall, this study demonstrates that quantum-enhanced components can contribute complementary predictive signals within hybrid drug discovery workflows, even under the constraints of current Noisy Intermediate-Scale Quantum (NISQ) hardware.
The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein in the Receptor Tyrosine Kinases (RTKs) family, playing a central role in cell growth, motility, and differentiation. Mutations in EGFR are associated with various cancers, notably Non-Small Cell Lung Cancer (NSCLC), due to overexpression and dysregulated signaling. To support early-stage drug discovery targeting EGFR, this work introduces Q-CaDD (Quantum-enhanced Computer-aided Drug Design), a hybrid quantum–classical computational framework. Q-CaDD integrates quantum machine learning (QML), classical models, molecular docking, and multi-stage ligand filtering. Over 200,000 ligands were generated using STONED-SELFIES and filtered using Ghose and Quantitative Estimate of Drug-Likeness (QED) criteria. Docking simulations were performed using AutoDock Vina, and toxicity prediction employed an ensemble of machine learning models including a Quantum Support Vector Machine (QSVM). Model evaluation was conducted on the Tox21 dataset using the NR-AR (Androgen Receptor) pathway as a proof-of-concept toxicity endpoint. The hybrid ensemble achieved an AUC-ROC of ~ 0.76, demonstrating improved generalisation relative to standalone classical models under the same evaluation setting and competitive performance with respect to reported benchmark results. Several compounds exhibited favourable predicted affinity and toxicity profiles and are identified as preliminary candidate molecules pending further experimental validation. Overall, this study demonstrates that quantum-enhanced components can contribute complementary predictive signals within hybrid drug discovery workflows, even under the constraints of current Noisy Intermediate-Scale Quantum (NISQ) hardware.
This study examines how quantum computing (QC) is being applied to molecular design and drug discovery. This study aims to investigates how QC surpasses classical limitations, focusing on empirical performance in precision, accuracy, and optimisation tasks. Study design use PRISMA 2009 guidelines, 15 empirical studies (2020-2025) were included. Data were extracted on the drug-discovery stage, the algorithm used, evaluation metrics, benefits, and limitations. The findings show QC outperforms classical methods particularly through hybrid quantum–classical models. Thirteen studies reported superior gains, including AUC–ROC values of 0.80–0.95, +30% improvement in drug-likeness (QED), +6% increase in prediction accuracy, and up to 99% accuracy in drug–target interaction tasks. However, noisy intermediate-scale quantum (NISQ) hardware limitations and poor scalability limit real-world deployment, due to noise, and limited qubit counts. Consequently, current performance results are largely simulation-based rather than hardware-validated. In contrast to prior algorithm-centric reviews, this study provides a consolidated empirical synthesis and proposes a hybrid quantum–classical pipeline that maps high-performing algorithms across the drug discovery workflow under NISQ-era constraints. These findings inform pharmaceutical research and development by identifying realistic adoption pathways and the boundaries of current technological readiness.
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.
MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, and poses formidable challenges in pharmaceutical R&D. In recent years, emerging technologies like artificial intelligence (AI), big data, and cloud technologies have garnered great attention to predict the ADME and toxicity of molecules. Currently, the blend of quantum computation and machine learning has attracted considerable attention in almost every field ranging from chemistry to biomedicine and several engineering disciplines as well. Quantum computers have the potential to bring advances in high-throughput experimental techniques and in screening billions of molecules by reducing development costs and time associated with the drug discovery process. Motivated by the efficiency of quantum kernel methods, we proposed a quantum machine learning (QML) framework consisting of a classical support vector classifier algorithm with a kernel-based quantum classifier. To demonstrate the feasibility of the proposed QML framework, the simplified molecular input line entry system (SMILES) notation-based string kernel, combined with a quantum support vector classifier, is used for the evaluation of chemical/drug ADME-Tox properties. The proposed quantum machine learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the quantum model achieved the best performance as compared to classical counterparts in terms of the area under the curve of the receiver operating characteristic curve (AUC ROC; 0.80-0.95) for predicting outcomes on ADME-Tox data sets for small molecules, with a different number of features. The deployment of the proposed framework in the pharmaceutical industry would be extremely valuable in making the best decisions possible.
Q2SAR: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning
Quantitative Structure-Activity Relationship (QSAR) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and costly late-stage clinical failures. In this paper, we present a Quantum Multiple Kernel Learning (QMKL) framework, dubbed Next-Gen Q2SAR, that leverages Quantum Support Vector Machines (QSVMs) to overcome these classical limitations. By encoding molecular descriptors into exponentially large quantum Hilbert spaces, our approach substantially enhances the expressiveness of non-linear modeling. Benchmarking our quantum-enhanced framework on a dataset targeting the DYRK1A kinase (a critical target for Alzheimer's disease), the QMKL-SVM achieves an impressive Area Under the Curve (AUC) score of 0.8750, significantly outperforming classical state-of-the-art Gradient Boosting models (AUC=0.8037). Furthermore, we establish a theoretical and empirical pathway toward resolving classical data bottlenecks through projected quantum kernels (PQK) and measurement accelerators. As quantum computing architecture matures, this framework paves the way for autonomous cognitive architectures and self-improving drug discovery pipelines, promising to unlock deeper insights across vast chemical spaces and to accelerate the development of life-saving therapeutics.
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.