  Geometric deep learning has been revolutionizing the molecular modeling
field. Despite the state-of-the-art neural network models are approaching ab
initio accuracy for molecular property prediction, their applications, such as
drug discovery and molecular dynamics (MD) simulation, have been hindered by
insufficient utilization of geometric information and high computational costs.
Here we propose an equivariant geometry-enhanced graph neural network called
ViSNet, which elegantly extracts geometric features and efficiently models
molecular structures with low computational costs. Our proposed ViSNet
outperforms state-of-the-art approaches on multiple MD benchmarks, including
MD17, revised MD17 and MD22, and achieves excellent chemical property
prediction on QM9 and Molecule3D datasets. Additionally, ViSNet achieved the
top winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition.
Furthermore, through a series of simulations and case studies, ViSNet can
efficiently explore the conformational space and provide reasonable
interpretability to map geometric representations to molecular structures.
