MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability | Paper Citation Record

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This paper cites Nature Chemical Biology 20(8), 960–973 (2024) 22.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Nature Chemical Biology 20(8), 960–973 (2024) 22

Reference 1

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This paper cites Artificial Intelligence in Drug Design, 447–460 (2022).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Artificial Intelligence in Drug Design, 447–460 (2022)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:22.011829Z

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This paper cites Nature Machine Intelligence 2(10), 573–584 (2020).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Nature Machine Intelligence 2(10), 573–584 (2020)

Reference 3

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This paper cites Nature 616(7958), 673–685 (2023).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Nature 616(7958), 673–685 (2023)

Reference 4

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This paper cites : Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability : Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism

Reference 5

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This paper cites : Analyzing learned molecular repre- sentations for property prediction.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability : Analyzing learned molecular repre- sentations for property prediction

Reference 6

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This paper cites DeeperGCN: All You Need to Train Deeper GCNs.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability DeeperGCN: All You Need to Train Deeper GCNs

Reference 7

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This paper cites Advances in neural information processing systems 33, 12559–12571 (2020).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Advances in neural information processing systems 33, 12559–12571 (2020)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.774748Z

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This paper cites Nature Machine Intelligence 4(3), 279–287 (2022).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Nature Machine Intelligence 4(3), 279–287 (2022)

Reference 9

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This paper cites Briefings in bioinformatics 23(6), 408 (2022).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Briefings in bioinformatics 23(6), 408 (2022)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.635993Z

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This paper cites Nature Machine Intelligence 4(2), 127–134 (2022).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Nature Machine Intelligence 4(2), 127–134 (2022)

Reference 11

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This paper cites Briefings in Bioinformatics 24(5), 306 (2023).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Briefings in Bioinformatics 24(5), 306 (2023)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.600529Z

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This paper cites Communications Chemistry 6(1), 34 (2023).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Communications Chemistry 6(1), 34 (2023)

Reference 13

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This paper cites Machine learning meets quantum physics, 199–214 (2020) 23.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Machine learning meets quantum physics, 199–214 (2020) 23

Reference 14

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This paper cites : Graph neural networks for materials science and chemistry.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability : Graph neural networks for materials science and chemistry

Reference 15

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This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 16

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This paper cites In: International Conference on Learning Representations (2021).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability In: International Conference on Learning Representations (2021)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.555118Z

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This paper cites Neural Computation 9, 1735–1780 (1997).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Neural Computation 9, 1735–1780 (1997)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.543929Z

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This paper cites xLSTM: Extended Long Short-Term Memory.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability xLSTM: Extended Long Short-Term Memory

Reference 19

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This paper cites Attention Is All You Need.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Attention Is All You Need

Reference 20

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This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 21

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This paper cites Journal of cheminformatics 9, 1–7 (2017).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Journal of cheminformatics 9, 1–7 (2017)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.532943Z

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This paper cites In: ICML.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability In: ICML

Reference 23

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This paper cites Multi-Head Mixture-of-Experts.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Multi-Head Mixture-of-Experts

Reference 24

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This paper cites In: ICLR (Poster) (2017).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability In: ICLR (Poster) (2017)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.501973Z

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This paper cites Journal of Chemical Information and Modeling 63(1), 43–55 (2022) 24.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Journal of Chemical Information and Modeling 63(1), 43–55 (2022) 24

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.318903Z

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This paper cites ChemMedChem 3(10), 1503 (2008).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability ChemMedChem 3(10), 1503 (2008)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.203016Z

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This paper cites Neural computation 3(1), 79–87 (1991).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Neural computation 3(1), 79–87 (1991)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.064971Z

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This paper cites Advances in neural information processing systems 35, 23049–23062 (2022).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Advances in neural information processing systems 35, 23049–23062 (2022)

Reference 29

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This paper cites https://www.rdkit.org.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability https://www.rdkit.org

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.040170Z

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This paper cites Journal of Chemical Information and Modeling 62(22), 5361–5372 (2022).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Journal of Chemical Information and Modeling 62(22), 5361–5372 (2022)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.028359Z

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This paper cites Advances in neural information processing systems 33, 18661–18673 (2020).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Advances in neural information processing systems 33, 18661–18673 (2020)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:21.007819Z

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This paper cites Chemical science 9(2), 513–530 (2018).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Chemical science 9(2), 513–530 (2018)

Reference 33

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This paper cites Journal of chemical information and modeling 56(4), 763–773 (2016).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Journal of chemical information and modeling 56(4), 763–773 (2016)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:20.991647Z

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This paper cites International Ophthalmology 44(1), 214 (2024).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability International Ophthalmology 44(1), 214 (2024)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:20.980348Z

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This paper cites StatPearls Publishing, Treasure Island (FL), ??? (2023).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability StatPearls Publishing, Treasure Island (FL), ??? (2023)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:20.969922Z

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This paper cites Machine learning 45, 5–32 (2001).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Machine learning 45, 5–32 (2001)

Reference 37

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This paper cites Bioorganic & medicinal chemistry 24(22), 5842–5854 (2016).

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability Bioorganic & medicinal chemistry 24(22), 5842–5854 (2016)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T23:33:20.953409Z

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This paper cites MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability.

MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability

Reference 39

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