BioLLMNet: enhancing RNA-interaction prediction with a specialized cross-LLM transformation network

Ribonucleic acids (RNAs) play a central role in cellular processes by interacting with proteins, small molecules, and other RNAs. Accurate prediction of these interactions is critical for understanding post-transcriptional regulation and advancing RNA-targeted therapeutics. However, existing computational methods are limited by their reliance on hand-crafted features, modality-specific architectures, and often require structural or physicochemical data, which are experimentally challenging to obtain and unavailable for many RNA molecules. These constraints hinder generalizability and fail to capture the complex, context-dependent semantics of RNA interactions. We present BioLLMNet, a unified sequence-only framework that leverages pretrained biological language models to encode rich, contextualized representations for both RNA molecules and their interacting partners, including proteins, small molecules, and other RNAs. Our key innovation is the introduction of a novel learnable gating mechanism, which dynamically computes feature-wise weights to adaptively integrate multimodal embeddings based on input context. This mechanism, proposed here for the first time in RNA interaction modeling, enables the model to emphasize the most informative features from each partner and achieves seamless fusion of heterogeneous modalities. As a result, BioLLMNet represents a unified framework and can flexibly and consistently model all three types of interaction (RNA-protein, RNA-small molecule, and RNA-RNA) within a shared architecture, eliminating the need for modality-specific designs. Comprehensive evaluations on benchmark data sets demonstrate that BioLLMNet achieves state-of-the-art performance across all three types of interaction. Our results underscore the power of language model-based representations combined with dynamic feature fusion for generalizable, modality-aware RNA interaction prediction.