Ginex, TizianaVázquez Lozano, JavierEstarellas, CarolinaLuque Garriga, F. Xavier2025-12-182025-12-182024-06-040959-440Xhttps://hdl.handle.net/2445/225051The 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. 9 p.application/pdfengcc-by-nc (c) Ginex, Tiziana et al., 2024http://creativecommons.org/licenses/by-nc/4.0/Disseny de medicamentsBioquímica quànticaDrug designQuantum biochemistryinfo:eu-repo/semantics/article7490472025-12-18info:eu-repo/semantics/openAccess38914031