Prediction of Atropisomerism for Drug-like Molecules

A multistep computational workflow that accurately assigns
organic
drug-like molecules to one of three atropisomer classes on the basis
of computed barrier heights has been developed. The workflow identifies
rotatable bonds and applies progressively more accurate types of calculations
to the eligible rotational degrees of freedom. An initial energy scan
with a force field (OPLS4) is followed by a similar scan that uses
an energy function driven by a neural network model (QRNN-TB) trained
on density functional theory (DFT) energies. The maxima corresponding
to the potentially stereogenic rotatable bonds identified at this
point are further processed by applying a transition state search
at the QRNN-TB level of theory. Finally, ωB97X-D3/def2-TZVP(-f)
DFT energies are computed for all located extrema. The accuracy of
the predicted rotational barriers was benchmarked against ωB97M-V/cc-pVTZ
and DLPNO-CCSD(T)/def2-TZVPP energies with excellent correlations.
The automated protocol classifies organic molecules into atropisomeric
classes with a greater than 90% success rate when applied to a test
set of 65 molecules containing rotationally restricted torsions (68
torsions in total). We anticipate that the balance of speed and accuracy
in this method will make it conducive to production use in drug discovery
programs.