The paper maps the tiered graph autoencoder and its variational variant onto PyTorch Geometric components, and proposes a data pipeline with standard chemical identifiers.
Probabilistic Generative Deep Learning for Molecular Design
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abstract
Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep learning approach. It leverages the existing huge databases and publications of experimental results, and quantum-mechanical calculations, to learn and explore molecular structure, properties and activities. We discuss the major components of probabilistic generative deep learning for molecular design, which include molecular structure, molecular representations, deep generative models, molecular latent representations and latent space, molecular structure-property and structure-activity relationships, molecular similarity and molecular design. We highlight significant recent work using or applicable to this new approach.
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cs.LG 1years
2019 1verdicts
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Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
The paper maps the tiered graph autoencoder and its variational variant onto PyTorch Geometric components, and proposes a data pipeline with standard chemical identifiers.