Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing

Yusong Wang, Tong Wang (), Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao () and Tie-Yan Liu
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Yusong Wang: Microsoft Research AI4Science
Tong Wang: Microsoft Research AI4Science
Shaoning Li: Microsoft Research AI4Science
Xinheng He: Microsoft Research AI4Science
Mingyu Li: Microsoft Research AI4Science
Zun Wang: Microsoft Research AI4Science
Nanning Zheng: Xi’an Jiaotong University
Bin Shao: Microsoft Research AI4Science
Tie-Yan Liu: Microsoft Research AI4Science

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract 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. 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.

Date: 2024
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DOI: 10.1038/s41467-023-43720-2

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