2026-04-14T02:47:34Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/2250512026-04-08T14:42:53Zcom_2072_1057col_2072_478917col_2072_478924
RECERCAT author Ginex, Tiziana author Vázquez Lozano, Javier author Estarellas, Carolina author Luque Garriga, F. Xavier 2025-12-18T12:06:18Z2025-12-18T12:06:18Z2024-06-042025-12-18T12:06:18Z The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small- sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM- tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing perfor- mance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formi- dable, but we will undoubtedly see impressive advances that will define a new era.  cc-by-nc (c) Ginex, Tiziana et al., 2024 http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Disseny de medicaments Bioquímica quàntica Drug design Quantum biochemistry Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion