Molecule Graph Networks with Many-Body Equivariant Interactions.

ABSTRACT: Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity by capturing geometric data symmetries, thereby improving model accuracy. However, two-body bond vectors in opposition may cancel each other out during message passing, leading to the loss of directional information on their shared node. In this study, we develop Equivariant N-body Interaction Networks (ENINet) that explicitly integrates l = 1 equivariant many-body interactions to enhance directional symmetric information in the message passing scheme. We provided a mathematical analysis demonstrating the necessity of incorporating many-body equivariant interactions and general

SUBMITTER: Mao Z 

PROVIDER: S-EPMC12392447 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature