However, for the last 25 years, theoretical developments, better computational algorithms, faster computing resources, and improved visualization tools enabled the routine use of computational methods to model and visualize protein-ligand (PL) interactions, calculate binding free energy to different degrees of accuracy, and in silico screen chemical libraries using ligand-based and structure-based approaches. In spite of multiple efforts to improve its performance, drug discovery remains a costly and time consuming technique ( Phatak et al., 2009). For many years, both the identification and optimization of novel drug lead compounds were accomplished within the drug discovery process by the experimental high-throughput screening of large chemical libraries. The cost to bring a new drug to the market could be as high as 2.6 billion US dollars, and can take up to 15 years ( DiMasi et al., 2016). It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. Most docking programs are rooted in classical molecular mechanics. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. 3Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina.2Facultad de Ciencias Biomédicas and Facultad de IngenierÃa, Universidad Austral, Pilar, Argentina.1Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Argentina.
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