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Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks.
1903
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2022
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2020
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2020
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2020
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2021
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2022
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2022
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F. Sestak, L. Schneckenreiter, S. Hochreiter, A. Mayr, and G. Klambauer, “VN-EGNN: Equivariant graph neural networks with virtual nodes enhance protein binding site identification,” in ELLIS Machine Learning for Molecules Workshop 2023 (2023)
2023
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A. Antelmi, G. Cordasco, M. Polato, V. Scarano, C. Spagnuolo, and D. Yang, “A Survey on Hypergraph Representation Learning,” ACM Computing Surveys (2023), 10.1145/3605776 , just Accepted
2023
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K. M. Saifuddin, B. Bumgardner, F. Tanvir, and E. Akbas, “HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network,” in 2023 IEEE 39th International Conference on Data Engineering (ICDE) (2023) pp. 1503–1516, iSSN: 2375-026X
2023
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V. Bhat, P. Sornberger, B. S. S. Pokuri, R. Duke, B. Ganapathysubramanian, and C. Risko, “Electronic, redox, and optical property prediction of organic π \pi -conjugated molecules through a hierarchy of machine learning approaches,” Chemical Science 14
2023
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