SMALTR gateway is a Science Gateway for Structure-based MAchine Learning Tools for RNA (and other biomolecules) deployed with Apache Airavata ScIGaP gateway platform.The gateway uses machine learning to develop tools for multi-scale modeling of RNA structure. The work is motivated by an interest in elucidating links between RNA sequence, structure, and function to advance of fundamental understanding biophysical processes in the cell as well as design effective small-molecule probes of RNA.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. Using a combination of computer docking, classical molecular dynamics simulations, and transfer learning, the project team develop and deployed a 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.