Enabling in-silico Hit Discovery Workflows Targeting RNA with Small Molecules

Ribonucleic acids (RNAs) are emerging as important drug targets, yet in silico modeling of RNA-small molecule interactions presents unique challenges compared to protein targets. This study evaluates and improves computational tools within the Schrödinger Suite for hit discovery against RNA. We benchmarked and enhanced SiteMap for identifying RNA binding sites, introducing an RNA-specific scoring function that improved site prediction accuracy from 44.4% to 65.3% on a filtered HARIBOSS dataset. We also refined Glide, Schrödinger's molecular docking tool, by rebalancing scoring function weights for RNA systems, resulting in improved pose prediction accuracy to 73.7% on a recently published dataset. Furthermore, we expanded the applicability of absolute binding free energy perturbation (ABFEP) to nucleic acid receptors, demonstrating good correlation with experimental binding affinities (RMSE of 1.10 kcal/mol) on a diverse set of 5 RNA-ligand systems. These advancements provide a promising foundation for structure-based drug discovery targeting RNA, although further refinements are needed to achieve performance levels comparable to protein targets, particularly in addressing RNA's dynamic nature and docking into apo receptors.