Leveraging Quantum Annealing for Ligand Modelling in Drug Discovery

Drug discovery is an intricate and multifaceted process that necessitates the identification and development of novel medications to combat various illnesses. This convoluted procedure typically encompasses various stages, including fundamental research, preclinical research, clinical research, and FDA approval. Notwithstanding the indispensability of the drug discovery process, it is time-consuming and exorbitant, with low success rates often being the norm. In this paper, we endeavour to provide a comprehensive appraisal of the divergent methodologies employed in drug discovery, inclusive of the wet lab approach and the classical computer-based approach. The wet lab approach requires extensive experimentation within laboratory settings to pinpoint potential drug candidates, whereas the classical computer-based approach employs computational techniques to simulate and prognosticate the properties of potential drug compounds. Despite the merits of both approaches, they are not without limitations, which we shall delve into in the course of this discourse. The multifariousness of the drug discovery process and the sheer volume of data generated during the course of experimentation necessitate the use of advanced technologies and algorithms in enhancing the process's performance. This paper aims to furnish an overview of the present state of drug discovery, while also underscoring the need for relentless research and innovation in this domain.