Molecular simulation and drug design in the quantum era

Raffaele Santagati, Alán Aspuru‐Guzik, Ryan Babbush +12 more

Charnelle Razo, Belinda Mutunhu Ndlovu

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.

Avishek Ray, Poulami Das, Siddhartha Bhattacharyya +3 more

Lovely Chaurasia, Shikha Parmar, Shivani Sharma, Archana Sharma, Renu Virk, Ajay Dixit, Meenakshi Tyagi

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.

Mostafijur Rahman Mamun

Abstract : We propose a hybrid quantum–classical framework that integrates variational quantum machine learning (QML) for molecular binding affinity prediction with quantum annealing for Pareto-optimal drug formulation design. Leveraging a four-qubit variational circuit, our QML model achieves superior predictive performance compared to state-of-the-art classical baselines, effectively capturing complex molecular interactions. In parallel, we formulate drug formulation optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using D-Wave’s quantum annealer, enabling efficient identification of Pareto-optimal solutions that balance bioavailability, toxicity, and stability. By unifying predictive modeling and combinatorial optimization within a single pipeline, our framework demonstrates the potential of near-term quantum devices to accelerate and enhance critical stages of drug discovery. These findings underscore the transformative role of NISQ-era algorithms in bridging molecular informatics and formulation design, paving the way for scalable, data-driven pharmaceutical innovation.

R. Suresh Kumar, V Sambasiva Reddy, G Neha Chowdary +3 more