Paper Citation Record · LEDGER

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training

As of 17 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.01376.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.01376 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:52:19.710696Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact2
verified fuzzy8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a3f943b-1611-4620-aa5a-e6f95c6f4310 · outbound

This paper cites E., and Bailey-Kellogg, C.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training E., and Bailey-Kellogg, C

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:21.053315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 343c63aa-dd29-4cca-89a0-9c8384711b63 · outbound

This paper cites An adaptive workflow coupled with random forest algorithm to identify intact n-glycopeptides detected from mass spectrometry.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training An adaptive workflow coupled with random forest algorithm to identify intact n-glycopeptides detected from mass spectrometry

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:20.855610Z

Source-reported events for the cited work

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Observation 8adce9be-c22b-4f73-9fd0-55a3bce4984d · outbound

This paper cites N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:20.635907Z

Source-reported events for the cited work

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Observation 083e14fc-d6be-4f00-914c-f9567714a423 · outbound

This paper cites Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

Reference 11

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Source-reported events for the cited work

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Observation 64ac253a-d2ae-4857-933f-3602e70de20c · outbound

This paper cites A Systematic Survey of Chemical Pre-trained Models.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training A Systematic Survey of Chemical Pre-trained Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:19.295112Z

Source-reported events for the cited work

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Observation de7ac643-b571-43ce-986b-63eab0d589d5 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training How Powerful are Graph Neural Networks?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:19.353159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a378bea9-07fa-40d1-be34-7ff4f0b41cb7 · outbound

This paper cites GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:52:20.107182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 23f9fc6a-47d0-4d75-bcd6-a6fccb599752 · outbound

This paper cites an unresolved cited work.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 7ddaba76-d231-4aee-8cdc-b5e0a85197be · outbound

This paper cites TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:52:19.941181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0b56d699-ffee-4839-944d-ed06c26b24ff · outbound

This paper cites Appendix A.1.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Appendix A.1

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:20.387426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c6b36d4e-c811-40ee-937c-6740b552adf1 · outbound

This paper cites Strategies for Pre-training Graph Neural Networks.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Strategies for Pre-training Graph Neural Networks

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:18.848358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc69a14-6ba1-4474-9b95-0a22a69acbb9 · outbound

This paper cites Composition-based Multi-Relational Graph Convolutional Networks.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Composition-based Multi-Relational Graph Convolutional Networks

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:19.193433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b19ee9b-fde9-4c34-aa07-a43231c856b8 · outbound

This paper cites Language models of protein sequences at the scale of evolution enable accurate structure prediction.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Language models of protein sequences at the scale of evolution enable accurate structure prediction

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:20.746977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f042d69f-b450-4ee1-acf6-0278e793d8b8 · outbound

This paper cites Graph Attention Networks.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Graph Attention Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:19.241288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05919400-238e-401d-8b22-db4752602a3a · outbound

This paper cites Is Transfer Learning Necessary for Protein Landscape Prediction?.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Is Transfer Learning Necessary for Protein Landscape Prediction?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:19.086609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6641180a-acc1-4daf-9651-570d559a0223 · outbound

This paper cites Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:18.809284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80b28b69-810f-49e7-905e-f3edb74cfaff · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:18.719194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e001dc95-3829-47f1-9302-0c81f689ab0c · outbound

This paper cites M., and Collins, J.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training M., and Collins, J

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:21.141349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fca6757c-5c23-4d0b-b37c-e25ebec51dcf · outbound

This paper cites J., Oktay, D., Lin, Z., Verkuil, R., Tran, V.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training J., Oktay, D., Lin, Z., Verkuil, R., Tran, V

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:20.955392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fab61e61-6c87-41cb-a417-609efa3d3d67 · outbound

This paper cites Generrna: A generative pre-trained language model for de novo rna design.

Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training Generrna: A generative pre-trained language model for de novo rna design

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:20.505813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.