  Unraveling the relation between the chemical structure of small drug-like
compounds and their rate of passive permeation across lipid membranes is of
fundamental importance for pharmaceutical applications. The elucidation of a
comprehensive structure-permeability relationship expressed in terms of a few
molecular descriptors is unfortunately hampered by the overwhelming number of
possible compounds. In this work, we reduce a priori the size and diversity of
chemical space to solve an analogous---but smoothed out---structure-property
relationship problem. This is achieved by relying on a physics-based
coarse-grained model that reduces the size of chemical space, enabling a
comprehensive exploration of this space with greatly reduced computational
cost. We perform high-throughput coarse-grained (HTCG) simulations to derive a
permeability surface in terms of two simple molecular descriptors---bulk
partitioning free energy and pKa. The surface is constructed by exhaustively
simulating all coarse-grained compounds that are representative of small
organic molecules (ranging from 30 to 160 Da) in a high-throughput scheme. We
provide results for acidic, basic and zwitterionic compounds. Connecting back
to the atomic resolution, the HTCG predictions for more than 500,000 compounds
allow us to establish a clear connection between specific chemical groups and
the resulting permeability coefficient, enabling for the first time an inverse
design procedure. Our results have profound implications for drug synthesis:
the predominance of commonly-employed chemical moieties narrows down the range
of permeabilities.
