Title: LiRDSC: Ligand-Conditioned RNA Sequence Design via Diffusive Structural Conditioning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: RNA design, RNA-Ligand, Structure

Abstract: RNA design for specific small-molecule ligands is a central goal in molecular engineering, yet computational approaches are hindered by two fundamental challenges: extreme sensitivity to the fidelity of input tertiary structure scaffolds and a tendency toward ``mode collapse'', where models generate generic rather than ligand-specific sequences. To address this, we present two core contributions. First, we introduce RLData2400, a benchmark dataset combining high-resolution experimental structures with diverse, high-confidence \textit{in silico} models to facilitate the development of models. Second, we propose \textbf{LiRDSC (Ligand-conditioned RNA Design via Diffusive Structural Conditioning)}, a deep generative framework architected for specificity. LiRDSC uniquely employs a \textbf{Diffusive Structural Encoder (DSE)}, which learns resilient representations by training on noise-perturbed structures, and a \textbf{Ligand-Contextual FiLM Conditioner (LCFC)} that steers the model to reason about the ligand’s identity, preventing mode collapse. Trained on our dataset (RLData2400), LiRDSC not only achieves high sequence recovery but also generates a vast diversity of ligand-target RNA sequences. Crucially, its superiority is most pronounced on structurally augmented data, directly validating the robustness imparted by our diffusion-based conditioning. Inverse folding experiments further confirm that these designs accurately recapitulate their target tertiary structures, and importantly, computational analysis predicts high binding compatibility with the target ligands, demonstrating  LiRDSC’s ability to produce structurally viable and ligand-specific RNA candidates.