In silico medicinal chemistry investigates molecular systems that are too large to be tackled by medium to high level ab initio quantum chemistry. Only atomistic force fields can deliver rapid computation of energy required in sampling the many conformational and orientational degrees of freedom of a ligand within a protein pocket. However, the predictive reliability of a force field critically depends on the quality and realism of its energy function. Particularly, the electrostatic component of this energy needs to be as accurate as possible because druglike ligands and proteins are polar molecules, whose interaction does not just depend on shape. Surprisingly, the challenging problem of energy accuracy receives much less attention than it deserves. Docking results in the literature are still dependent on atomic point charges, which are inherently inaccurate at short and medium range. This has been known for decades but improved and more accurate methods have not (yet) found their way in mainstream in silico medicinal chemistry. Moreover, often the “details” of the electrostatic energy are poorly and not at all reported, as if they do not matter. This article attempts to inspire future docking algorithms with ideas from an approach called Quantum Chemical Topology (QCT). The way this method partitions energy and treats the electrostatic interaction should inject more realism into the current paradigm. The gap between the medicinal chemistry “world view” and that of physical and computational chemistry needs to narrow en route to reach the currently elusive goal to make docking work for the right reasons. We discuss in detail a path to make electrostatics drastically more realistic, based on novel ideas, some partially implemented.
Keywords: Quantum chemical topology (QCT), docking, proteins, amino acids, polarisation, multipole moments, electron density, kriging, machine learning, quantum theory of atoms in molecules, atomic partial charges, energy partitioning
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