Quantum Computing in Drug Discovery: A Comparative Analysis of Classical, Quantum, and Hybrid Approaches

Abstract

Drug discovery is a complex, costly, and data-intensive process that depends increasingly on computational methods for molecular modelling, virtual screening, property prediction, and lead optimisation. Classical high-performance computing, molecular simulation, and machine-learning methods currently provide the practical foundation for computer-aided drug discovery; however, their accuracy and scalability can be constrained when electronic correlation, large conformational spaces, or combinatorial optimisation must be treated explicitly. Quantum computing offers an alternative computational paradigm in which molecular electronic states and selected optimisation problems may be represented more naturally. This narrative comparative review examines the respective roles of classical, quantum, and hybrid classical-quantum approaches across major drug-discovery tasks. The analysis considers computational accuracy, scalability, hardware maturity, data requirements, interpretability, workflow integration, and evidence of practical utility. Current evidence indicates that classical methods remain superior for routine high-throughput screening and production-scale modelling, whereas quantum methods are presently restricted mainly to small molecular systems, proof-of-concept studies, and carefully selected subproblems. Variational quantum eigensolvers, quantum machine-learning models, and hybrid optimisation workflows are promising, but their performance remains sensitive to noise, circuit depth, data encoding, active-space selection, and benchmark design. The most credible near-term pathway is therefore not wholesale replacement of classical computing, but targeted integration of quantum routines into validated classical pipelines. Progress toward pharmaceutical utility will require chemically meaningful benchmarks, fair comparisons against strong classical baselines, uncertainty reporting, reproducible software stacks, and experimental validation of quantum-generated candidates.