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Paper Citation Record · LEDGER

Geometric Deep Learning on Molecular Representations

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.12375.

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

pith.paper-citation-record.v1
2107.12375 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:20.041021Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T03:11:19.204579Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 88630349-7ca3-4fe0-b0af-67e30eca77b8 · inbound

Geometric deep learning assists protein engineering. Opportunities and Challenges cites this paper.

Geometric deep learning assists protein engineering. Opportunities and Challenges Geometric Deep Learning on Molecular Representations

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:20.041021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:48:20.041021Z digest=sha256:113d2b92e3cc0d1d7dbdea9df7e8c3f197619b22db11b7e287a6dd138716cb8b

Observation b2787e32-d6e2-4fc0-bfa0-ff401544a46e · inbound

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators cites this paper.

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators Geometric Deep Learning on Molecular Representations

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-05-12T03:11:19.205947Z

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.

source=arxiv_source observed=2026-05-12T03:08:27.746140Z digest=sha256:60ec75140fb651442d33e83906185e55c64a09b6e9cad25354bfd44a408ad01a