Data-Driven Modeling of RNA Interactions with Small-Molecule Ligands

Computational methods play a central role in drug discovery, enabling virtual screening, structure optimization, and compound activity profiling. Historically, medicinal chemistry has focused almost exclusively on protein-ligand interactions, and computational tools have been developed accordingly. However, the discovery of functional RNAs and their therapeutic potential has sparked growing interest in RNAs as drug targets. Despite this shift, bioinformatics tools for RNA remain limited compared to those available for proteins. I will present our recent approaches to modelling RNA-small molecule interactions, which combine experimental data with machine learning. These methods address a significant shortcoming in the computational toolkit for RNA-targeted drug discovery.