Description: In the cell, ribonucleic acids (RNAs) interact with ligands such as proteins and small molecules, and these interactions modulate essential cellular processes like RNA transcription, splicing, modification, translation, degradation, and nuclear export. Determining the structure of RNA, RNA-protein, or RNA-small molecule complexes, as well as the thermodynamics and kinetics of their association and dissociation,​ is critical in understanding how such complexes modulate cellular processes. Biophysical characterization using experimental techniques, however, can be tedious and technically non-trivial. Alternatively, computational methods can be used to more rapidly and cost-effectively characterize biomolecular complexes like RNA-containing complexes. But, existing computational methods either exhibit low accuracy or require substantial expertise and computational resources. Using a combination of computer docking, classical molecular dynamics simulations, and transfer learning, the PI will develop and deploy fast and easy-to-use data-driven software tools to rapidly and accurately predict the structure of RNA-protein and RNA-small molecule complexes as well as to estimate the binding affinities, their rates of dissociation, and by extension, their rates of association, directly from their structure.