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.