Geometric deep learning on molecular representations

By Kenneth Atz, Francesca Grisoni, Gisbert Schneider. 2021

410 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Shuo Zhang
2. Yang Liu
3. Lei Xie
4. Kenneth Atz

In Materials Chemistry

1. Shuo Zhang
2. Yang Liu
3. Lei Xie
4. Kenneth Atz

Citations in the corpus

• Artificial Intelligence For Natural Product Drug Discovery (2023, neutral): “123.”
• Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes (2022, positive): “by treating each 3D structure as a graph and performing message passing scheme on it [1, 18, 34, 41, 43].”
• Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023, background): “…medicinal chemistry include Article https://doi.org/10.1038/s41557-023-01360-5 various machine learning methods developed for chemical reaction planning 23,29,30 , GNNs have been successfully employed for retrosynthe-sis planning, regioselectivity prediction and reaction product prediction 31–34 .”