We report QDπ-v1.0 for modeling the internal energy
of drug
molecules containing H, C, N, and O atoms. The QDπ model is
in the form of a quantum mechanical/machine learning potential correction
(QM/Δ-MLP) that uses a fast third-order self-consistent density-functional
tight-binding (DFTB3/3OB) model that is corrected to a quantitatively
high-level of accuracy through a deep-learning potential (DeepPot-SE).
The model has the advantage that it is able to properly treat electrostatic
interactions and handle changes in charge/protonation states. The
model is trained against reference data computed at the ωB97X/6-31G*
level (as in the ANI-1x data set) and compared to several other approximate
semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3,
MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is
demonstrated to be accurate for a wide range of intra- and intermolecular
interactions (despite its intended use as an internal energy model)
and has shown to perform exceptionally well for relative protonation/deprotonation
energies and tautomers. An example application to model reactions
involved in RNA strand cleavage catalyzed by protein and nucleic acid
enzymes illustrates QDπ has average errors less than 0.5 kcal/mol,
whereas the other models compared have errors over an order of magnitude
greater. Taken together, this makes QDπ highly attractive as
a potential force field model for drug discovery.