RNAClip: Auxiliary Protein–Ligand Data Improves Pocket-Free RNA–Ligand Virtual Screening

Small molecules targeting RNA offer therapeutic opportunities beyond conventional protein targets, yet current screening methods are constrained by the difficulty of locating binding pockets and the scarcity of validated RNA–ligand interactions. Here, we introduce , a contrastive retrieval framework that brings abundant protein–ligand data into RNA virtual screening. By aligning receptors and ligands in a shared embedding space, the framework enables sequence-based screening without a predefined RNA pocket or binding pose. Auxiliary protein examples contribute both paired supervision and contrastive negatives, allowing RNA screening to draw on data beyond the RNA domain. We also construct a new evaluation set from recently released RNA–ligand structures in the Protein Data Bank(PDB). On this strictly independent test set, outperforms all other evaluated models, achieving state-of-the-art(SOTA) screening performance. A series of ablation experiments further demonstrates that a small subset of auxiliary protein–ligand data can match the performance achieved with the full corpus, and that using auxiliary examples solely as contrastive negatives can preserve the gains from joint training.