AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules.

ABSTRACT: While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex biomolecular data sets. The Aw

SUBMITTER: Szocinski T 

PROVIDER: S-EPMC8263495 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature