  Graph neural networks have become a powerful framework for learning complex
structure-property relationships and fast screening of chemical compounds.
Recently proposed methods have demonstrated that using 3D geometry information
of the molecule along with the bonding structure can lead to more accurate
prediction on a wide range of properties. A common practice is to use 3D
geometries computed through density functional theory (DFT) for both training
and testing of models. However, the computational time needed for DFT
calculations can be prohibitively large. Moreover, many of the properties that
we aim to predict can often be obtained with little or no overhead on top of
the DFT calculations used to produce the 3D geometry information, voiding the
need for a predictive model. To be practically useful for high-throughput
chemical screening and drug discovery, it is desirable to work with 3D
geometries obtained using less-accurate but much more efficient non-DFT
methods. In this work we investigate the impact of using non-DFT conformations
in the training and the testing of existing models and propose a data
augmentation method for improving the prediction accuracy of classical
forcefield-derived geometries.
