Combining the flexibility of deep learning with the represenational power of graph scattering transforms to study thermodynamic landscapes within RNA folding

Geometric Scattering Autoencoder 2020
Egbert Castro, Andrew Benz, Alexander Tong, Guy Wolf, Smita Krishnaswamy
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Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing biomolecular graphs in ways that expose meaningful relations and variations between them. We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings. Our embedding network first extracts rich graph features using the recently proposed geometric scattering transform. Then, it leverages a semi-supervised variational autoencoder to extract a low-dimensional embedding that retains the information in these features that enable prediction of molecular properties as well as characterize graphs. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting bistable RNA structures. Also, the model is generative and can sample new folding trajectories.

Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings image