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

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.

SUBMITTER: Wang Y

PROVIDER: S-EPMC10770089 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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

Wang Yusong Y Wang Tong T Li Shaoning S He Xinheng X Li Mingyu M Wang Zun Z Zheng Nanning N Shao Bin B Liu Tie-Yan TY

Nature communications 20240105 1

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 geome ...[more]

PMID: 38182565

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Dataset Information

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

Publications

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

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