Targeting RNA with small molecules using state-of-the-art methods provides highly predictive affinities of riboswitch inhibitors.

Targeting RNA with small molecules represents a promising yet relatively unexplored avenue for the design of new drugs. Nevertheless, challenges arise from the lack of computational models and techniques able to accurately model RNA systems, and predict their binding affinities to small molecules. Here, we tackle these difficulties by developing a tailored state-of-the-art approach for absolute binding free energy calculations of RNA-binding small molecules. For this, we combine the advanced AMOEBA polarizable force field to the newly developed lambda-Adaptive Biasing Force scheme associated to refined restraints allowing for efficient sampling. To capture the free energy barrier associated to challenging RNA conformational changes, we combine machine learning-based collective variables with enhanced sampling simulations. Applying this computational protocol to a complex Riboswitch-like RNA target demonstrates quantitative predictions. These results pave the way for the routine application of free energy simulations in RNA-targeted drug discovery, thus providing a significant reduction in their failure rate.