  Predicting quantum chemical properties is a fundamental challenge for
computational chemistry. While the development of graph neural networks has
advanced molecular representation learning and property prediction, their
performance could be further enhanced by incorporating 3D structural geometry
into 2D molecular graph representation. In this study, we introduce the
PointGAT model for quantum molecular property prediction, which integrates 3D
molecular coordinates with graph-attention modeling. Comparison with other
current models in molecular prediction tasks showed that PointGAT could provide
higher predictive accuracy in various benchmark datasets from MoleculeNet,
including ESOL, FreeSolv, Lipop, HIV, and 10 out of 12 tasks of the QM9
dataset. To further examine PointGAT prediction of quantum mechanical (QM)
energies, we constructed a C10 dataset comprising 11,841 charged and chiral
carbocation intermediates with QM energies calculated at the
DM21/6-31G*//B3LYP/6-31G* levels. Notably, PointGAT achieved an R2 value of
0.950 and an MAE of 1.616 kcal/mol, outperforming other models. Additional
ablation studies indicated that incorporating molecular geometry into the model
resulted in markedly higher predictive accuracy, reducing the MAE value from
1.802 kcal/mol to 1.616 kcal/mol. Moreover, visualization of PointGAT atomic
attention weights suggested its predictions were interpretable. Findings in
this study support the application of PointGAT as a powerful and versatile tool
for quantum chemical property prediction that can facilitate high-accuracy
modeling for fundamental exploration of chemical space as well as drug design
and molecular engineering.
