Paper Citation Record · LEDGER

Toward Better Geometric Representations for Molecule Generative Models

As of 4 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2605.07693.

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

pith.paper-citation-record.v1
2605.07693 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T03:13:50.490077Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:02:29.665449Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

59 of 59 outbound references displayed

verified exact15
verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da473d33-0055-4ffc-bf7c-066f27ac4f0c · outbound

This paper cites Deep generative molecular design reshapes drug discovery.Cell Reports Medicine, 3(12).

Toward Better Geometric Representations for Molecule Generative Models Deep generative molecular design reshapes drug discovery.Cell Reports Medicine, 3(12)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.131254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 861397d2-ece5-4aee-b32e-4adc75a89905 · outbound

This paper cites Molecular design in drug discovery: a comprehensive review of deep generative models.Briefings in bioinformatics, 22(6):bbab344.

Toward Better Geometric Representations for Molecule Generative Models Molecular design in drug discovery: a comprehensive review of deep generative models.Briefings in bioinformatics, 22(6):bbab344

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.129387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e939bac6-0dac-4d37-99f3-9f957ef51158 · outbound

This paper cites A survey of generative ai for de novo drug design: new frontiers in molecule and protein generation.Briefings in Bioinformatics, 25(4):bbae338.

Toward Better Geometric Representations for Molecule Generative Models A survey of generative ai for de novo drug design: new frontiers in molecule and protein generation.Briefings in Bioinformatics, 25(4):bbae338

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.088001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 88a0d31c-5530-4498-bcb0-433767d75a51 · outbound

This paper cites Smiles, a chemical language and information system.

Toward Better Geometric Representations for Molecule Generative Models Smiles, a chemical language and information system

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.123516Z

Source-reported events for the cited work

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Observation c8fae437-be09-46ce-a5eb-274895c5a7c5 · outbound

This paper cites Self-referencing embedded strings (selfies): A 100% robust molecular string representation.

Toward Better Geometric Representations for Molecule Generative Models Self-referencing embedded strings (selfies): A 100% robust molecular string representation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.137167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 96c81f80-e801-4d14-898f-7025e62ce64f · outbound

This paper cites Group selfies: a robust fragment-based molecular string representation.Digital Discovery, 2(3):748–758.

Toward Better Geometric Representations for Molecule Generative Models Group selfies: a robust fragment-based molecular string representation.Digital Discovery, 2(3):748–758

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.096010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 17f40698-12f1-4c18-b5a6-919814435130 · outbound

This paper cites Equivariant diffusion for molecule generation in 3d.International Conference on Machine Learning, pages 9087–9102.

Toward Better Geometric Representations for Molecule Generative Models Equivariant diffusion for molecule generation in 3d.International Conference on Machine Learning, pages 9087–9102

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.090439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0af2dbc2-9730-41fa-86b1-6eb5bf969833 · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

Toward Better Geometric Representations for Molecule Generative Models GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.684937Z

Source-reported events for the cited work

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Observation bcae0916-cac6-4424-ba22-1ba00349ead3 · outbound

This paper cites MolVision: Molecular Property Prediction with Vision Language Models.

Toward Better Geometric Representations for Molecule Generative Models MolVision: Molecular Property Prediction with Vision Language Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.638751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 329b7f7d-dc4c-4320-a080-9bc8e8e48aa9 · outbound

This paper cites an unresolved cited work.

Toward Better Geometric Representations for Molecule Generative Models Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-14T10:54:13.155169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 80642a90-8f74-4dbb-8c95-434a9f11c1a8 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851.

Toward Better Geometric Representations for Molecule Generative Models Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.148252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8931cdb9-2fa8-4cf2-a14b-d6e8c97c969d · outbound

This paper cites Denoising Diffusion Implicit Models.

Toward Better Geometric Representations for Molecule Generative Models Denoising Diffusion Implicit Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-11T03:15:54.652852Z

Source-reported events for the cited work

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Observation db0d2057-316b-410a-b182-462370f77e55 · outbound

This paper cites Flow Matching for Generative Modeling.

Toward Better Geometric Representations for Molecule Generative Models Flow Matching for Generative Modeling

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-11T03:15:54.566024Z

Source-reported events for the cited work

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Observation 8e795de7-060d-4cc8-8abb-3e1b83c9d4a6 · outbound

This paper cites De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100.

Toward Better Geometric Representations for Molecule Generative Models De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.153597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation afe9d613-a042-4eaf-b6d2-423c41347fac · outbound

This paper cites DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.

Toward Better Geometric Representations for Molecule Generative Models DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:15:54.605348Z

Source-reported events for the cited work

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Observation 0f835a79-0543-43c3-8b37-889839aa8369 · outbound

This paper cites Applications of deep learning in molecule generation and molecular property prediction.Accounts of chemical research, 54(2):263–270.

Toward Better Geometric Representations for Molecule Generative Models Applications of deep learning in molecule generation and molecular property prediction.Accounts of chemical research, 54(2):263–270

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.157269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 09f4205d-5c0d-4af7-a074-54259de86243 · outbound

This paper cites Self-driving laboratories for chemistry and materials science.Chemical Reviews, 124(16):9633–9732.

Toward Better Geometric Representations for Molecule Generative Models Self-driving laboratories for chemistry and materials science.Chemical Reviews, 124(16):9633–9732

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.151895Z

Source-reported events for the cited work

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Observation 72503a84-5b48-4620-9a54-aea2a295df95 · outbound

This paper cites Advances and challenges in deep generative models for de novo molecule generation.

Toward Better Geometric Representations for Molecule Generative Models Advances and challenges in deep generative models for de novo molecule generation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.163086Z

Source-reported events for the cited work

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Observation 83feb5fd-8f0a-4cd1-82ca-316ac4153153 · outbound

This paper cites Flowmol3: flow matching for 3d de novo small-molecule generation.Digital Discovery.

Toward Better Geometric Representations for Molecule Generative Models Flowmol3: flow matching for 3d de novo small-molecule generation.Digital Discovery

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.150021Z

Source-reported events for the cited work

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Observation ff375c7a-7024-41ae-ae03-98a3e35be64a · outbound

This paper cites Propmolflow: property-guided molecule generation with geometry-complete flow matching.Nature Computa- tional Science, pages 1–10.

Toward Better Geometric Representations for Molecule Generative Models Propmolflow: property-guided molecule generation with geometry-complete flow matching.Nature Computa- tional Science, pages 1–10

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.100162Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation be4ebc04-e0ff-4889-a216-cde6508cf99d · outbound

This paper cites Applications of modular co-design for de novo 3d molecule generation.Digital Discovery, 5(2):754–768.

Toward Better Geometric Representations for Molecule Generative Models Applications of modular co-design for de novo 3d molecule generation.Digital Discovery, 5(2):754–768

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.146263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 27c658ae-6217-4d38-a9b9-88d743767c11 · outbound

This paper cites 3d molecule generation from rigid motifs via se (3) flows.

Toward Better Geometric Representations for Molecule Generative Models 3d molecule generation from rigid motifs via se (3) flows

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.707342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6cab40a5-8cfd-43e3-bae7-edd33f4f019b · outbound

This paper cites Bidirectional molecule generation with recurrent neural networks.Journal of chemical information and modeling, 60(3):1175–1183.

Toward Better Geometric Representations for Molecule Generative Models Bidirectional molecule generation with recurrent neural networks.Journal of chemical information and modeling, 60(3):1175–1183

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.105396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d60ba545-2427-4fe7-b3cf-bba6d7d43502 · outbound

This paper cites Geometric latent diffusion models for 3d molecule generation.

Toward Better Geometric Representations for Molecule Generative Models Geometric latent diffusion models for 3d molecule generation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.109257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b76381af-227a-42ab-a8b3-6ee3b55bb7f0 · outbound

This paper cites Geometric Representation Condition Improves Equivariant Molecule Generation.

Toward Better Geometric Representations for Molecule Generative Models Geometric Representation Condition Improves Equivariant Molecule Generation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.571466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5ff31cb0-1a6d-4473-b7a7-6a8dc91e3216 · outbound

This paper cites Diffusion model as representation learner.

Toward Better Geometric Representations for Molecule Generative Models Diffusion model as representation learner

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.103484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 29f67d24-d132-46f7-a24c-6d58baf46f45 · outbound

This paper cites Uni-mol: A universal 3d molecular representation learning framework.

Toward Better Geometric Representations for Molecule Generative Models Uni-mol: A universal 3d molecular representation learning framework

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.140548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f3a20a84-d549-4117-827f-cd91a41586fe · outbound

This paper cites Fractional denoising for 3d molecular pre-training.International Conference on Machine Learning.

Toward Better Geometric Representations for Molecule Generative Models Fractional denoising for 3d molecular pre-training.International Conference on Machine Learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.133268Z

Source-reported events for the cited work

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Observation 999b9fb1-8288-499f-bca5-a1f89557a7dd · outbound

This paper cites Return of unconditional generation: A self- supervised representation generation method.Advances in Neural Information Processing Systems, 37:125441–125468.

Toward Better Geometric Representations for Molecule Generative Models Return of unconditional generation: A self- supervised representation generation method.Advances in Neural Information Processing Systems, 37:125441–125468

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.135072Z

Source-reported events for the cited work

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Observation 4fdda98a-afd7-4915-991e-72597a98cce9 · outbound

This paper cites Georecon: Graph-level representation learning for 3d molecules via reconstruction-based pretraining.

Toward Better Geometric Representations for Molecule Generative Models Georecon: Graph-level representation learning for 3d molecules via reconstruction-based pretraining

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.537863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 82986241-0016-43c2-b194-97a0e66ef5f9 · outbound

This paper cites Multi-modal molecular representation learning via structure awareness.IEEE Transactions on Image Processing.

Toward Better Geometric Representations for Molecule Generative Models Multi-modal molecular representation learning via structure awareness.IEEE Transactions on Image Processing

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.142543Z

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Observation 3e61d227-99dd-4f88-afb9-f9f71938b7bb · outbound

This paper cites Image style transfer using convolutional neural networks.

Toward Better Geometric Representations for Molecule Generative Models Image style transfer using convolutional neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.127203Z

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Observation 495d0c67-3953-41f7-afea-880f56d9530c · outbound

This paper cites Feature pyramid networks for object detection.

Toward Better Geometric Representations for Molecule Generative Models Feature pyramid networks for object detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.138862Z

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Observation 4d6daaa1-1e57-4351-87c6-92b74fceee97 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.European Conference on Computer Vision, pages 694–711.

Toward Better Geometric Representations for Molecule Generative Models Perceptual losses for real-time style transfer and super-resolution.European Conference on Computer Vision, pages 694–711

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.092218Z

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Observation d64facd2-a736-48df-aa9a-a21eb2f8be73 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Toward Better Geometric Representations for Molecule Generative Models Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T15:09:37.504223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5e55234d-6904-4ca0-82c8-3c17db8fd6c4 · outbound

This paper cites fDOLwOcAA+yedyTcERK5GoVcJuo=.

Toward Better Geometric Representations for Molecule Generative Models fDOLwOcAA+yedyTcERK5GoVcJuo=

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:15:54.514640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ee23cc1c-29d0-4fcf-9f8e-e2f5f9be4121 · outbound

This paper cites Geometry-complete diffusion for 3D molecule gen- eration and optimization.Communications Chemistry, 7(1):150.

Toward Better Geometric Representations for Molecule Generative Models Geometry-complete diffusion for 3D molecule gen- eration and optimization.Communications Chemistry, 7(1):150

Reference 37

Resolution: malformed identifier; raw_fallback, observed 2026-05-14T10:54:13.094160Z

Source-reported events for the cited work

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Observation 2bf9e87b-4e69-407c-ad97-e397bc789a44 · outbound

This paper cites Midi: Mixed graph and 3d denoising diffusion for molecule generation.European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.

Toward Better Geometric Representations for Molecule Generative Models Midi: Mixed graph and 3d denoising diffusion for molecule generation.European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.159228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5dfb9c6e-17a7-4aea-8824-b2f7296ac5a0 · outbound

This paper cites Equivariant flow matching with hybrid probability transport for 3d molecule generation.Advances in Neural Information Processing Systems, 36:549–568.

Toward Better Geometric Representations for Molecule Generative Models Equivariant flow matching with hybrid probability transport for 3d molecule generation.Advances in Neural Information Processing Systems, 36:549–568

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.098255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 35b04236-7e16-4167-a673-ce2b477e43be · outbound

This paper cites Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport.

Toward Better Geometric Representations for Molecule Generative Models Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.697344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3e9aba4d-e5f5-45c2-9a29-84806fb96d60 · outbound

This paper cites 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction.

Toward Better Geometric Representations for Molecule Generative Models 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.557962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26573f98-b6bf-42a2-b104-b23551510269 · outbound

This paper cites Structure-based drug design with equivariant diffusion models.Nature Computational Science, 4(12):899–909.

Toward Better Geometric Representations for Molecule Generative Models Structure-based drug design with equivariant diffusion models.Nature Computational Science, 4(12):899–909

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.144310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a124bf9e-bfe5-4c68-aef9-220e39234860 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Toward Better Geometric Representations for Molecule Generative Models High- resolution image synthesis with latent diffusion models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.161159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a2af84dd-25b1-40aa-bcfe-95894450d898 · outbound

This paper cites Geometric latent diffusion models for 3D molecule generation.

Toward Better Geometric Representations for Molecule Generative Models Geometric latent diffusion models for 3D molecule generation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.119922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c5884184-ccf2-4738-8961-c32c09b36208 · outbound

This paper cites The unrea- sonable effectiveness of deep features as a perceptual metric.

Toward Better Geometric Representations for Molecule Generative Models The unrea- sonable effectiveness of deep features as a perceptual metric

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.107297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ccc4298b-d20f-4b4e-977b-76b07b888f40 · outbound

This paper cites Consistency models.

Toward Better Geometric Representations for Molecule Generative Models Consistency models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.118185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a01acf2a-a04f-4452-92e2-677e70fcb575 · outbound

This paper cites Spin-nerf: Multiview segmen- tation and perceptual inpainting with neural radiance fields.

Toward Better Geometric Representations for Molecule Generative Models Spin-nerf: Multiview segmen- tation and perceptual inpainting with neural radiance fields

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.121710Z

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Observation b1b6b7d0-645f-41c3-847c-b60cb378195e · outbound

This paper cites Learning diffusion models with flexible representation guidance.

Toward Better Geometric Representations for Molecule Generative Models Learning diffusion models with flexible representation guidance

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.110932Z

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Observation f9329dd3-4e0d-4154-8256-f56791ba5bf4 · outbound

This paper cites Pre-training via Denoising for Molecular Property Prediction.

Toward Better Geometric Representations for Molecule Generative Models Pre-training via Denoising for Molecular Property Prediction

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:15:54.544541Z

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Observation 3a439042-a15c-471a-9e00-27cd41b29152 · outbound

This paper cites Self-conditioned denoising for atomistic represen- tation learning.

Toward Better Geometric Representations for Molecule Generative Models Self-conditioned denoising for atomistic represen- tation learning

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.580838Z

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Observation 610d2528-1bc1-4ed7-9d21-af8c6e20d45a · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.Scientific Data, 1(1):1–7.

Toward Better Geometric Representations for Molecule Generative Models Quantum chemistry structures and properties of 134 kilo molecules.Scientific Data, 1(1):1–7

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.114632Z

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Observation f75373ce-2326-4348-b7f5-6e12615d066f · outbound

This paper cites Auto-encoding variational bayes.International Confer- ence on Learning Representations.

Toward Better Geometric Representations for Molecule Generative Models Auto-encoding variational bayes.International Confer- ence on Learning Representations

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.116440Z

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Observation 989b83b4-90c7-4255-9414-c40c654f4b17 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Toward Better Geometric Representations for Molecule Generative Models Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:30:06.834416Z

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Observation a02a82bb-1efa-4adc-8258-8dc60797b871 · outbound

This paper cites Geom, energy-annotated molecular conforma- tions for property prediction and molecular generation.Scientific Data, 9(1):185.

Toward Better Geometric Representations for Molecule Generative Models Geom, energy-annotated molecular conforma- tions for property prediction and molecular generation.Scientific Data, 9(1):185

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.112656Z

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Observation cdd49b80-db91-406b-a7a3-374e16cac4aa · outbound

This paper cites SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching.

Toward Better Geometric Representations for Molecule Generative Models SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.552349Z

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Observation 620aa158-e41c-4c72-b2ff-4a7ff28b2bd6 · outbound

This paper cites Mixed continuous and categorical flow matching for 3d de novo molecule generation.ArXiv, pages arXiv–2404.

Toward Better Geometric Representations for Molecule Generative Models Mixed continuous and categorical flow matching for 3d de novo molecule generation.ArXiv, pages arXiv–2404

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T10:54:13.125321Z

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Observation a9ec55a0-7ab0-428f-b240-a832cd92616a · outbound

This paper cites Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation.

Toward Better Geometric Representations for Molecule Generative Models Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.529063Z

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

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

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

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Observation 1f979c43-3378-43b3-a373-a8a4c98adc67 · outbound

This paper cites canonical slice.

Toward Better Geometric Representations for Molecule Generative Models canonical slice

Reference 59

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T03:15:54.676411Z

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Pith citing papers

Observation 23df4d7d-0549-4348-880e-fd57705be2a4 · 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 Toward Better Geometric Representations for Molecule Generative Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T22:02:29.665449Z

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