Equivariant Graph Attention Networks for Molecular Property Prediction
Equivariant Graph Attention Networks for Molecular Property Prediction
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage the geometric and relational detail of the problem domain and are known to learn expressive representations through t...
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Equivariant Graph Attention Networks for Molecular Property Prediction
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TN_cdi_proquest_journals_2631757143
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2631757143
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E-ISSN
2331-8422
