The DOI matches its Crossref record; 179 of the 179 references it cites were found in a registry or an index.
Geometric deep learning on molecular representations
Open accessAbstract
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.
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Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
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Metadata
- Authors
- Atz, K., Grisoni, F., & Schneider, G.
- Venue
- Nature Machine Intelligence
- Published
- 2021-12-15
- Volume
- 3
- Issue
- 12
- Pages
- 1023-1032
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3185087522
- Cited by
- 373
- References
- 179
- FWCI
- 25.62
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Funders
- ETH RETHINK Initiative
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (205321_182176)
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