Graph neural network and diffusion model for modeling RNA interatomic interactions.

ABSTRACT:

Motivation

Ribonucleic acid (RNA) function is inherently linked to its 3D structure, traditionally determined by X-ray crystallography, Nuclear Magnetic Resonance, and Cryo-EM. However, these techniques often lack atomic-level resolution, highlighting the need for accurate in silico RNA structure prediction tools. Current state-of-the-art methods, such as AlphaFold3, Boltz1, RhoFold, or trRosettaRNA, rely on deep learning models that represent residues as frames and use transformers to learn relative positions. While effective for known RNA families, their performance drops for synthetic or novel families.

Results

In this work, we explore the potential of graph neural networks and denoising diffusion probabilistic models for learning interatomic interactions. We model RNA as

SUBMITTER: Justyna M 

PROVIDER: S-EPMC12472125 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature