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Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
Tiered latent representations and latent spaces for molecular graphs pro...
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Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Classification
Tiered graph autoencoders provide the architecture and mechanisms for le...
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Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
Recent advances in artificial intelligence have propelled the developmen...
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Decoding Molecular Graph Embeddings with Reinforcement Learning
We present RL-VAE, a graph-to-graph variational autoencoder that uses re...
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Geometric Scattering Attention Networks
Geometric scattering has recently gained recognition in graph representa...
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Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
Extracting insight from the enormous quantity of data generated from mol...
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Learning data representation using modified autoencoder for the integrative analysis of multi-omics data
In integrative analyses of omics data, it is often of interest to extrac...
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Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings
Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. While numerous approaches aim to train classifiers that accurately predict molecular properties from graphs that encode their structure, an equally important task is to organize 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. Our approach is based on the intuition that geometric scattering generates multi-resolution features with in-built invariance to deformations, but as they are unsupervised, these features may not be tuned for optimally capturing relevant domain-specific properties. We demonstrate the effectiveness of our approach to data exploration of RNA foldings. Like proteins, RNA molecules can fold to create low energy functional structures such as hairpins, but the landscape of possible folds and fold sequences are not well visualized by existing methods. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting bistable RNA structures. Furthermore, it enables interpolation of embedded molecule sequences mimicking folding trajectories. Finally, using an auxiliary inverse-scattering model, we demonstrate our ability to generate synthetic RNA graphs along the trajectory thus providing hypothetical folding sequences for further analysis.
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