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Paper Citation Record · LEDGER

Latent Molecular Optimization for Targeted Therapeutic Design

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1809.02032.

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

pith.paper-citation-record.v1
1809.02032 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:26:50.267425Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T10:20:02.946283Z

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 3206e465-7863-41e1-8b77-6d55c6eeb59c · inbound

DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning cites this paper.

DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning Latent Molecular Optimization for Targeted Therapeutic Design

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-14T12:26:50.267425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T12:26:50.267425Z digest=sha256:b4b1832de14defa219a32c11a1110431a6c2f5775ce300d36e28a43e94d3f388

Observation d634367f-901d-454a-a569-fef2aa434f02 · inbound

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning cites this paper.

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Latent Molecular Optimization for Targeted Therapeutic Design

Reference 2020

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:20:02.952133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T10:20:02.638854Z digest=sha256:6db85a39da07d67846c1bb155be00e35936aca051a3614024d867ba49c208bc7