E(n) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules Article Swipe
Kieran Nehil-Puleo
,
Co D. Quach
,
Nicholas C. Craven
,
Clare MCabe
,
Peter T. Cummings
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jpcb.3c07304
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jpcb.3c07304
We have developed a multi-input E(n) equivariant graph convolution-based model designed for the prediction of chemical properties that result from the interaction of heterogeneous molecular structures. By incorporating spatial features and constraining the functions learned from these features to be equivariant to E(n) symmetries, the interactional-equivariant graph neural network (IEGNN) can efficiently learn from the 3D structure of multiple molecules. To verify the IEGNN's capability to learn interactional properties, we tested the model's performance on three molecular data sets, two of which are curated in this study and made publicly available for future interactional model benchmarking. To enable the loading of these data sets, an open-source data structure based on the PyTorch Geometric library for batch loading multigraph data points is also created. Finally, the IEGNN's performance on a data set consisting of an unknown interactional relationship (the frictional properties resulting between monolayers with variable composition) is examined. The IEGNN model developed was found to have the lowest mean absolute percent error for the predicted tribological properties of four of the six data sets when compared to previous methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jpcb.3c07304
- https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.3c07304
- OA Status
- hybrid
- Cited By
- 10
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390936165
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- OpenAlex ID
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https://openalex.org/W4390936165Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acs.jpcb.3c07304Digital Object Identifier
- Title
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E(n) Equivariant Graph Neural Network for Learning Interactional Properties of MoleculesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-17Full publication date if available
- Authors
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Kieran Nehil-Puleo, Co D. Quach, Nicholas C. Craven, Clare MCabe, Peter T. CummingsList of authors in order
- Landing page
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https://doi.org/10.1021/acs.jpcb.3c07304Publisher landing page
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https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.3c07304Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.3c07304Direct OA link when available
- Concepts
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Equivariant map, Graph, Computer science, Artificial neural network, Benchmarking, Homogeneous space, Set (abstract data type), Theoretical computer science, Artificial intelligence, Mathematics, Pure mathematics, Programming language, Geometry, Business, MarketingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 6, 2024: 4Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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