Quantum vs. Classical Approaches: A Comparative Analysis in the Context of Drug Development for Preclinical Applications

Abstract

Quantum computing is an emerging technology that leverages the principles of quantum mechanics to perform calculations exponentially faster than classical computers for certain applications. In drug discovery and development, quantum algorithms have the potential to accelerate the search for new therapeutics. This review provides a comparative analysis of quantum and classical computational approaches for drug development applications in the preclinical setting. We first give an overview of the principles behind quantum computing and explain how quantum circuits can encode and manipulate quantum information. We then discuss key quantum algorithms that may confer advantages over their classical counterparts for pharmaceutical problems such as molecular docking, molecular dynamics simulations, and machine learning. Current quantum computing hardware restrictions and the applicability of hybrid quantum-classical algorithms are also considered. We analyse early proof-of-concept demonstrations applying quantum methods to drug design problems and discuss the challenges and outlook moving forward. Overall, quantum computing holds promise to expand the scope and scale of computational modelling in drug discovery once the hardware matures, but classical techniques likely still have advantages for certain near-term applications. Further interdisciplinary research is needed to fully leverage the capabilities of quantum computation in the preclinical drug development pipeline.