Paper Citation Record · LEDGER

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2309.17296.

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

pith.paper-citation-record.v1
2309.17296 v2

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 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:12:19.004838Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:08.992581Z

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 69c0caf7-3129-4af9-946c-0e6a0af8078f · inbound

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering cites this paper.

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T22:12:19.004838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05ff4251-b21f-4927-87ce-f811410228e9 · inbound

TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality cites this paper.

TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T21:12:00.160931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37915611-054a-49b3-96a7-8d7e2fdf843c · inbound

Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation cites this paper.

Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T11:28:57.450739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e2decb7-4c75-4638-8968-c781700fcd7e · inbound

Toward Better Geometric Representations for Molecule Generative Models cites this paper.

Toward Better Geometric Representations for Molecule Generative Models Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.520173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7898cf5-2484-46fe-b4a4-eb3a0f8cbca6 · inbound

Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport cites this paper.

Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:17:08.994332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a4ea029a-6d1b-49ff-aedb-64696c95c7fd · 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 Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T22:02:28.814964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.