IRIS: A Machine Learning-Based Pose Reranking Tool for RNA-Ligand Docking

Given their fundamental roles in cellular processes and disease pathogenesis, RNA molecules are promising therapeutic targets. Predicting the 3D structure of RNA-ligand complexes using computational docking is a key element of rational, structure-based inhibitor design. However, RNA-ligand docking remains challenging, due in part to intrinsic properties of RNA such as structural flexibility and a highly charged phosphate backbone. rDock, a widely used RNA docking program, can generate ligand poses close to the experimental structure, but its scoring function frequently fails to rank these poses above less accurate alternatives. To supplement rDock, here we introduce the Intelligent RNA Interaction Scorer (IRIS), a regression model leveraging physicochemical and interaction-based features and trained on the largest data set of experimental nucleic acid-ligand complexes compiled to date for any ML-based tool designed for RNA docking (608 structures). IRIS improves rDock RNA-ligand pose ranking relative to the use of rDock scores alone. Using the best-performing rDock protocol on the RNA portion of the data set, we find that at least one of the 100 top generated poses for any given complex is within 2.0 Å RMSD of the native pose in 86.3% of test complexes. Of these 86.3%, the default rDock scoring function ranks the correct pose first in 42.7% of cases. IRIS improves this latter fraction to 59.8% and increases the success rate for selecting a near-native pose among the top five ranked poses from 64.6% to 78.0%. IRIS thus significantly enhances pose ranking accuracy and can be seamlessly integrated into docking pipelines to rerank ligand poses in RNA-targeted drug discovery.