Practical quantum advantage in drug discovery and development on today’s quantum resources

Quantum computing promises to revolutionize drug discovery and development by tackling chemical challenges beyond the capabilities of conventional methods. As the chemical complexity in drug discovery grows, and reliance on computational tools for early drug property assessment increases, efficient and accurate methods to assess these properties become essential. This paper explores the potential of quantum computing in this field and algorithms required for practical industrial applications. Specifically, we highlight the capabilities of qubitized downfolding, a quantum algorithm utilizing tensor-factorized Hamiltonian downfolding, through three challenging case studies. The first study investigates the polymorphic stability of ROY, a highly polymorphic compound, posing challenges for standard density functional theory (DFT) methods, alongside its 4'-fluoro analog. The second focuses on the macrocyclic drug Paritaprevir, a first-generation chameleonic HCV-NS3/4A protease inhibitor, and its analog, demonstrating how structural changes influence reaction transition state energetics, leading to varied metabolic profiles. The third study highlights differences in conformational flexibility and strain between Paritaprevir and Glecaprevir, a second-generation HCV-protease inhibitor, and it’s on impact potency across HCV genotypes. In all cases, qubitized downfolding demonstrates significantly improved quantum resource efficiency compared to current algorithms, highlighting its potential for execution on present-day quantum resources, ultimately establishing practical quantum computing applications for drug discovery and development challenges.