Paper Citation Record · LEDGER

MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2203.14500.

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

pith.paper-citation-record.v1
2203.14500 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:18:09.785975Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:15:33.946233Z

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 96b39e11-5fd9-4dd9-8026-f652ddf5c593 · inbound

Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems cites this paper.

Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:54:15.944560Z

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.

Observation 85a3ceb8-008c-4727-af40-c38dfcfae0cb · inbound

Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation cites this paper.

Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:15:33.949341Z

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.

Observation 114b6848-860b-4014-9f3f-96ab0b27213d · inbound

3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery cites this paper.

3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T20:18:09.785975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1642ed8-8550-445c-96c0-212127666e57 · inbound

ChemMLLM: Chemical Multimodal Large Language Model cites this paper.

ChemMLLM: Chemical Multimodal Large Language Model MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:04.931249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80c86d34-9321-4eeb-b296-565f83b75971 · inbound

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules cites this paper.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:17.961627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc4f6930-e8c2-46eb-aa1a-eb42a5f0dbea · inbound

Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces cites this paper.

Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:13.218334Z

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.