  We have developed a neural network potential energy function for use in drug
discovery, with chemical element support extended from 41% to 94% of druglike
molecules based on ChEMBL. We expand on the work of Smith et al., with their
highly accurate network for the elements H, C, N, O, creating a network for H,
C, N, O, S, F, Cl, P. We focus particularly on the calculation of relative
conformer energies, for which we show that our new potential energy function
has an RMSE of 0.70 kcal/mol for prospective druglike molecule conformers,
substantially better than the previous state of the art. The speed and accuracy
of this model could greatly accelerate the parameterization of protein-ligand
binding free energy calculations for novel druglike molecules.
