ENsiRNA: A multimodality method for siRNA-mRNA and modified siRNA efficacy prediction based on geometric graph neural network.

Abstract

With the rise of small interfering RNA (siRNA) as a therapeutic tool, effective siRNA design is crucial. Current methods often emphasize sequence-related features, overlooking structural information. To address this, we introduce ENsiRNA, a multimodal approach utilizing a geometric graph neural network to predict the efficacy of both standard and modified siRNA. ENsiRNA integrates sequence features from a pretrained RNA language model, structural characteristics, and thermodynamic data or chemical modification to enhance prediction accuracy. Our results indicate that ENsiRNA outperforms existing methods, achieving over a 13% improvement in Pearson Correlation Coefficient (PCC) for standard siRNA across various tests. For modified siRNA, despite challenges associated with RNA folding methods, ENsiRNA still demonstrates competitive performance in different datasets. This novel method highlights the significance of structural information and multimodal strategies in siRNA prediction, advancing the field of therapeutic design.