RNADiffFold: generative RNA secondary structure prediction using discrete diffusion models.

ABSTRACT: Ribonucleic acid (RNA) molecules are essential macromolecules that perform diverse biological functions in living beings. Precise prediction of RNA secondary structures is instrumental in deciphering their complex three-dimensional architecture and functionality. Traditional methodologies for RNA structure prediction, including energy-based and learning-based approaches, often depict RNA secondary structures from a static perspective and rely on stringent a priori constraints. Inspired by the success of diffusion models, in this work, we introduce RNADiffFold, an innovative generative prediction approach of RNA secondary structures based on multinomial diffusion. We reconceptualize the prediction of contact maps as akin to pixel-wise segmentation and accordingly train a denoising model to

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11586127 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature