Geometric Deep Learning on Molecular Representations

• URL: http://arxiv.org/abs/2107.12375v1
• Date: Mon, 26 Jul 2021 09:23:43 GMT
• Title: Geometric Deep Learning on Molecular Representations
• Authors: Kenneth Atz, Francesca Grisoni, Gisbert Schneider
• Abstract summary: Geometric deep learning (GDL) is based on neural network architectures that incorporate and process symmetry information. This review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction, and quantum chemistry. Emphasis is placed on the relevance of the learned molecular features and their complementarity to well-established molecular descriptors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial intelligence. GDL bears particular promise in molecular modeling applications, in which various molecular representations with different symmetry properties and levels of abstraction exist. This review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction, and quantum chemistry. Emphasis is placed on the relevance of the learned molecular features and their complementarity to well-established molecular descriptors. This review provides an overview of current challenges and opportunities, and presents a forecast of the future of GDL for molecular sciences.

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