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Paper Citation Record · LEDGER

Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.03494.

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

pith.paper-citation-record.v1
2410.03494 v1

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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:47:42.992064Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 2fb65fa6-c723-4ae8-b3e9-d05878804f38 · inbound

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors cites this paper.

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-03T04:47:42.992064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:47:42.992064Z digest=sha256:f604c3975447f21fb31ac2c6393156e7942845992a9c4e5311f44265c3a798c2

Observation 41b4acce-77c1-4a24-8ee8-eb4108363e01 · inbound

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models cites this paper.

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-01T13:06:13.242909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T13:06:13.242909Z digest=sha256:7bfe774f4666d91d6d9c6a1e5efb6e08ef75f99f785bff868a8723f947e5b792