Quantum computing promises to be a game changer for drug discovery and battery development as it will enable simulations of complex molecular and material systems not possible in classical regimes. Here, we introduce a hardware-aware, hybrid quantum-classical framework designed for practical pharmaceutical and energy problems. Extending state-of-the-art results from quantum machine learning, quantum-inspired optimization, and domain-specific datasets, the model improves both scalability and accuracy as well as interpretability. The proposed method is validated from real-drug and battery chemistry with advantages over the traditional method. This interdisciplinary breakthrough tackles major drawbacks of current quantum approaches and provides a realistic pathway towards industrial implementation in chemistry-led innovation.
Quantum computing promises to be a game changer for drug discovery and battery development as it will enable simulations of complex molecular and material systems not possible in classical regimes. Here, we introduce a hardware-aware, hybrid quantum-classical framework designed for practical pharmaceutical and energy problems. Extending state-of-the-art results from quantum machine learning, quantum-inspired optimization, and domain-specific datasets, the model improves both scalability and accuracy as well as interpretability. The proposed method is validated from real-drug and battery chemistry with advantages over the traditional method. This interdisciplinary breakthrough tackles major drawbacks of current quantum approaches and provides a realistic pathway towards industrial implementation in chemistry-led innovation.
The integration of quantum computing and artificial intelligence (AI), collectively termed Quantum AI, is revolutionizing the pharmaceutical industry by reshaping the paradigms of drug discovery and development. Traditional approaches to drug design are often limited by high costs, extensive timelines, and the computational intractability of simulating complex molecular interactions. Quantum computing offers exponential processing power, enabling the accurate modeling of quantum-level chemical phenomena that classical computers struggle to handle. Concurrently, AI contributes by enhancing data interpretation, prediction of molecular behaviors, and pattern recognition across large biological datasets. Together, Quantum AI enables faster identification of drug candidates, improved simulation of drug-target interactions, and early prediction of pharmacokinetics and toxicity. Key quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and quantum-enhanced machine learning, have demonstrated potential to outperform conventional methods in quantum chemical calculations. Moreover, this synergy facilitates personalized medicine by integrating patient-specific data to optimize therapeutic outcomes. Despite current challenges related to hardware scalability, algorithm optimization, and ethical integration, the future trajectory of Quantum AI is poised to redefine pharmaceutical innovation. This review underscores the transformative capabilities of Quantum AI and its potential to usher in a new era of efficient, cost-effective, and precision-driven drug development.
The drug discovery process has always been a problem for the pharmaceutical industry’s low rates of success, high expenses, and long-time frames. Quantum computing (QC) is being touted as a revolutionary advancement because of its unparalleled speed to carry out intricate multi molecular simulation and optimization. Unfortunately, its practical use has been suppressed due to the absence of proper hardware and the integration problems with traditional systems. Our solution is a quantum-classical pipeline for drug discovery. This pipeline utilizes machine learning (ML) for protein-ligand binding, drug property optimization, and even de novo drug design. In this paper we describe the pipeline, its components, the way it operates using NISQ devices, and the improvements it has in terms of accuracy and scalability (93.5% vs 78.2%) when compared with classical methods. Finally, we summarize how this can aid in modern drug discovery. It could significantly decrease the cost and timeline for pharmaceutical innovations.
Quantum AI, the integration of quantum computing and artificial intelligence, is revolutionizing drug design by addressing the limitations of traditional pharmaceutical development. This review explores its transformative potential in enhancing molecular simulations, structure-based, target-based, and ligand-based drug discovery, and personalized medicine. Quantum computing leverages principles like superposition, entanglement, and interference to perform complex calculations exponentially faster than classical systems, enabling precise modeling of molecular interactions. Algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) outperform classical methods like Density Functional Theory (DFT) in accuracy and efficiency, facilitating the identification of promising drug candidates and optimizing their pharmacokinetic properties. Quantum AI also enhances personalized medicine by analyzing vast genomic datasets to tailor treatments, improving efficacy and minimizing adverse effects. Despite challenges, including noisy intermediate-scale quantum (NISQ) device limitations and the need for advanced error correction, ongoing research and interdisciplinary collaboration are driving progress. Quantum AI promises to reduce development costs, accelerate drug discovery, and democratize access to life-saving medications. By enabling simulation-based discovery and expanding drug-candidate libraries to include peptides and antibodies, it paves the way for breakthroughs in treating complex diseases. This review underscores the need for continued investment in quantum technology to fully harness its potential, positioning the pharmaceutical industry for a future where drug development is faster, more precise, and patient-centric, ultimately improving global healthcare outcomes.
The integration of quantum computing in drug discovery has the potential to revolutionize pharmaceutical research by significantly enhancing molecular simulations, drug screening, and optimization processes. Traditional computational methods often struggle with the complexity of molecular interactions, leading to high costs and extended timelines in drug development. Quantum computing, with its ability to process complex calculations exponentially faster than classical computers, offers innovative solutions to these challenges. This chapter explores the fundamentals of quantum computing, its application in pharmaceutical research, and real-world case studies from leading organizations. It also discusses existing challenges, potential solutions, and future directions for adopting quantum computing in drug discovery. By addressing key technical, ethical, and regulatory aspects, this chapter provides a comprehensive analysis of how quantum technologies can accelerate and optimize the drug discovery pipeline.
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.
Over the past few years, artificial intelligence (AI) and quantum computing have emerged as groundbreaking technologies in the field of drug discovery. Techniques like AI-driven molecular design, quantum-enhanced simulations, and hybrid approaches have made significant strides in tackling the complexity of drug-target interactions. However, current models still face key challenges, such as the limited scalability of quantum systems, the difficulty of generalizing AI models due to insufficient data quality, and the inefficiencies in merging quantum and classical methods. To overcome these obstacles, this paper introduces a new hybrid quantum-AI workflow. By combining the precision of quantum computing for molecular simulations with the predictive power of AI, this approach aims to improve accuracy, scalability, and efficiency in drug discovery, paving the way for future breakthroughs in the field.