DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.

ABSTRACT: RNA-targeted therapeutics represent a promising frontier for expanding the druggable genome beyond conventional protein targets. However, computational prediction of RNA-compound interactions remains challenging due to limited experimental data and the inherent complexity of RNA structures. Here, we present DeepRNA-DTI, a novel sequence-based deep learning approach for RNA-compound interaction prediction with binding site interpretability. Our model leverages transfer learning from pretrained embeddings, RNA-FM for RNA sequences and Mole-BERT for compounds, and employs a multitask learning framework that simultaneously predicts both presence of interactions and nucleotide-level binding sites. This dual prediction strategy provides mechanistic insights into RNA-compound recognition patterns

SUBMITTER: Bae H 

PROVIDER: S-EPMC12777398 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature