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Computational methods play a key role in modern drug design in pharmaceutical industry but are mostly based on force fields which are limited in accuracy when describing non-classical binding effects, proton transfer or metal coordination. Here, we propose a general fully quantum mechanical (QM) scheme for the computation of protein-ligand affinities. It works on a single protein cutout (of about 1000 atoms) and evaluates all contributions (interaction energy, solvation, thermostatistical) to absolute binding free energy on the highest feasible QM level. The methodology is tested on two different protein targets: Activated serine protease factor X (FXa) and tyrosine-protein kinase 2 (TYK2). We demonstrate that the geometry of the model systems can be energy minimized efficiently by using general purpose graphics processing units, resulting in structures that are close to the co-crystallized protein-ligand structures. Our best calculations at a hybrid DFT level (PBEh-3c composite method) for the FXa ligand set result in an overall mean absolute deviation as low as 2.1 kcal/mol. Though very encouraging, an analysis of outliers indicates that the structure optimization level, conformational sampling as well as the solvation treatment requires further improvement.

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Stephan Ehrlich, Andreas H. Goller, and Stefan Grimme (2017)Contributed by Jan JensenErlich et al. presents absolute binding free energies for activated serine protease factor X (FXa) and tyrosine-protein kinase 2 predicted using DFT. Here I'll focus on...