Geometric deep learning on molecular representations

Geometric deep learning (GDL) is based on neural network architectures that incorporate and process symmetry information. GDL bears promise for molecular modelling applications that rely on molecular representations with different symmetry properties and levels of abstraction. This Review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction and quantum chemistry. It contains an introduction to the principles of GDL, as well as relevant molecular representations, such as molecular graphs, grids, surfaces and strings, and their respective properties. The current challenges for GDL in the molecular sciences are discussed, and a forecast of future opportunities is attempted. Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

Automated summary

Geometric deep learning, based on neural network architectures that process symmetry information, shows promise in molecular modeling applications. This review provides a detailed overview of molecular GDL, its applications, challenges, and future opportunities, highlighting the importance of geometric representations in molecular deep learning. Kenneth Atz and colleagues review the current progress and challenges of geometric deep learning in molecular sciences, emphasizing the significance of spatial structure information in molecules.