RLAgent: retrieval-augmented LLM agents for code-free RNA region-ligand interaction modeling

Accurately modeling RNA-ligand interactions is essential for understanding RNA function and for designing RNA-targeted therapeutics. Existing computational approaches mainly focus on predicting either full-length RNA structures or discrete binding sites, lacking explicit modeling of RNA regions that correspond to individual segments within larger, distributed, or structurally ambiguous binding motifs. In this study, we present RLAgent, an interactive agent-based framework that predicts RNA-ligand interactions at the regional level, offering a flexible and detailed modeling perspective, distinct from conventional site-centered approaches. RLAgent defines the RNA-ligand modeling process as an interactive, dialogue-driven workflow. Through a natural language interface, users can specify analysis goals and modeling settings without programming experience. A locally deployed large language model (LLM) serves as the core agent, managing all stages of the modeling pipeline, including data validation, feature representation, model training, performance evaluation, and result visualization. We further evaluated the impact of integrating retrieval-augmented generation (RAG) with RLAgent. The agent-based design of RLAgent reduces technical complexity, improves reproducibility, and makes RNA-ligand modeling more accessible to both computational and experimental researchers.

The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00483-6.