DLRNA-BERTa: a transformer approach for predicting molecule-RNA binding affinities

Therapies targeting RNA are rapidly expanding, with 24 FDA-approved RNA therapeutics and over 130 currently in clinical trials, highlighting RNA's growing role in drug discovery. In this context, transformer-based language models provide a scalable and cost-effective approach to accelerate RNA-targeted drug discovery by enabling the prediction of molecule-RNA binding affinities directly from sequence information. This study introduces DLRNA-BERTa, a RoBERTa-based framework that integrates RNA-BERTa and ChemBERTa-v2 to model molecule-RNA binding affinities. The framework includes six RNA class-specific models, aptamers, repeats, ribosomal RNAs, riboswitches, microRNAs, and viral RNAs, along with a general model for other RNA classes. DLRNA-BERTa outperformed existing approaches across different RNA classes on a randomly split validation dataset. On an independent and relatively diverse test set, the model achieved AUROC values of 0.57-0.59, demonstrating performance comparable to the other evaluated methods. Application of DLRNA-BERTa to a library of 3492 approved drugs identified 2859 compounds with predicted binding affinities (pKd ≥ 6) across 294 RNA targets, suggesting its potential utility for RNA-based drug repurposing and prioritization of candidate molecules for further investigation. To facilitate broader use and reproducibility, we provide a publicly accessible web application and API are available at https://huggingface.co/spaces/IlPakoZ/DLRNA-BERTa, enabling users to predict binding affinities between custom compounds and RNA sequences.