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Paper Citation Record · LEDGER

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.09730.

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

pith.paper-citation-record.v1
2408.09730 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:42:56.986538Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:46:08.296478Z

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 a74c4e54-ccae-40fc-892d-7806a1f52373 · inbound

Multimodal Medical Code Tokenizer cites this paper.

Multimodal Medical Code Tokenizer Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-09T00:42:56.986538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T00:42:56.986538Z digest=sha256:29c74c3d1ea3da466565729374306666e72aa95f663f067839411f7628003e2a

Observation 96321409-ed0b-46cc-8597-90144724827f · inbound

InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames cites this paper.

InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T07:22:13.227872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:22:13.227872Z digest=sha256:59088a86ae5e0d846d0786912fb0af29c85d1a18a37d6c46540590b36fd80975

Observation 006a55fa-516c-4624-a47a-048409932e9d · inbound

CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation cites this paper.

CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:46:08.298425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T15:50:05.070462Z digest=sha256:711a03025778753119bc58d5fcdfc08f2c599ecea0bedcd9f77194d6500ec3be

Observation 4341fa60-e0d4-4ef9-b9c9-4bbafa7e0285 · inbound

Atomic Design Transformer: Scaffold-Conditioned 3D Molecule Generation via xTB-Reward Reinforcement Learning cites this paper.

Atomic Design Transformer: Scaffold-Conditioned 3D Molecule Generation via xTB-Reward Reinforcement Learning Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-01T22:02:31.150696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:02:31.150696Z digest=sha256:396302c1b928537275ce1c8994f5921ebfeb6c62b460b504a168ea573395f130