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PresentationsQM/MM approach in inhibitors designResearch Center of Biotechnology RAS, Russia, 119071, Moscow, Leninsky Prospekt 33/2, Phone: (495) 954-52-83, E-mail: a.krivitskaya@fbras.ru 1Lomonosov Moscow State University, Faculty of Chemistry, Department of Physical Chemistry, Russia, 119234, Moscow, Leninskie Gory 1 Modern computer-aided drug discovery is a multi-stage process that includes target identification, detection of potential molecules and optimization, and preclinical testing. The search for potential molecules is typically based on structure-based or ligand-based drug design and involves hundreds of thousands to millions of compounds. The search for potential molecules traditionally involves methods such as docking, modeling and pharmacophore mapping, building structure-property or structure-activity relationships. The efficiency of these drug discovery methods currently remains quite low. This may be due to the insufficiently protein-ligand complex preparation and the fact that most methods for assessing binding use evaluation functions based on classical force fields of molecular mechanics. A more accurate complex and increased accuracy of binding assessment prediction can be obtained by performing quantum chemical calculations. In this sense, the QM/MM approach to searching for potential molecules is promising. However, QM/MM has not yet had a widespread impact on structure-based drug design. This is mostly due to its high computational cost. A dilemma arises: either the analysis of hundreds of thousands of compounds with often ineffective predictions, or the most accurate assessments of interactions for a very small set of molecules are possible. However, it is known that more than half of the drugs introduced to the market in 2023 are analogues of already known and used drugs [1]. Such analogues are called "next in class". An alternative search method based on the analysis of electron density obtained from quantum chemical calculations is suitable for searching for "next in class". This study presents examples of the application of the QM/MM approach to explain the mechanisms of inhibition of bacterial β-lactamase enzymes and the search for new, more effective inhibitors. This work was supported by the Russian Science Foundation (project № 19-73-20032). We acknowledge the use of supercomputer resources of the Joint Supercomputer Center of the Russian Academy of Sciences and the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University.
References 1. FDA’s Center for Drug Evaluation and Research’s (CDER). Advancing Health Through Innovation: New Drug Therapy Approvals 2023, №13, 2024, 1-33 P.
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