Paper Citation Record · LEDGER

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2507.20243.

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

pith.paper-citation-record.v1
2507.20243 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:45:54.292306Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8d329789-7fbc-4865-8274-20d956a03bcf · outbound

This paper cites Accurate structure prediction of biomolecular interactions with alphafold 3.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Accurate structure prediction of biomolecular interactions with alphafold 3

Reference 1

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Observation dc9f9ebb-b672-42e8-a09a-4073451d0139 · outbound

This paper cites Adjuvanting a subunit covid-19 vaccine to induce protective immunity.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Adjuvanting a subunit covid-19 vaccine to induce protective immunity

Reference 2

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Observation 532cfb18-a760-43a0-8f52-db127877b9a1 · outbound

This paper cites The protein data bank.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design The protein data bank

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:58.858980Z

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Observation 69727248-cdd8-4048-94ac-94b29a927103 · outbound

This paper cites Bronstein, and Alexander Tong.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Bronstein, and Alexander Tong

Reference 4

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This paper cites The probability flow ode is provably fast.Advances in Neural Information Processing Systems, 36:68552–68575, 2023.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design The probability flow ode is provably fast.Advances in Neural Information Processing Systems, 36:68552–68575, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:58.441607Z

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Observation aa0c71b6-200a-4c77-a537-ad32bd82d736 · outbound

This paper cites Single- sequence protein structure prediction using a language model and deep learning.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Single- sequence protein structure prediction using a language model and deep learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:58.244168Z

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Observation b6f2b4d3-30f1-4058-a98b-1fe9d6f2c883 · outbound

This paper cites An all-atom protein generative model.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design An all-atom protein generative model

Reference 7

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Observation 82d75096-4e3b-4510-a7e1-f9dd21076445 · outbound

This paper cites Robust deep learning–based protein sequence design using proteinmpnn.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Robust deep learning–based protein sequence design using proteinmpnn

Reference 8

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Observation 7b6906b5-c8db-42e2-afc4-3f30f7a1261a · outbound

This paper cites Pytorch lightning.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Pytorch lightning

Reference 9

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Observation 9daf686a-673f-4d53-a20e-c0d69abc381a · outbound

This paper cites One Step Diffusion via Shortcut Models.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design One Step Diffusion via Shortcut Models

Reference 10

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Observation cf7515e0-853b-447c-84ad-f85082c9f918 · outbound

This paper cites Denoising diffusion probabilistic models.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Denoising diffusion probabilistic models

Reference 11

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Observation 4017ecaf-d547-47a4-98d6-05950beec08a · outbound

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Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Unresolved cited work

Reference 13

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Observation c1fbdf31-4c13-420a-8511-8fd0e4f2cce4 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Highly accurate protein structure prediction with alphafold

Reference 14

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Observation b43d1fc4-7e92-442c-8bc8-2dae1eed5837 · outbound

This paper cites Mathematical methods of organizing and planning production.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Mathematical methods of organizing and planning production

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:57.473233Z

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Observation 26482f60-9c2e-4008-9a31-1852456e73ad · outbound

This paper cites Degiacomi, and Chris G.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Degiacomi, and Chris G

Reference 16

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Observation cee8feef-fd7f-4079-9226-8ef278374496 · outbound

This paper cites Introduction to Riemannian manifolds, volume 2.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Introduction to Riemannian manifolds, volume 2

Reference 17

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Observation 883ba735-d5cd-4151-afc9-ddac30b5058e · outbound

This paper cites Generating novel, designable, and diverse protein structures by equivariantly diffusing oriented residue clouds.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Generating novel, designable, and diverse protein structures by equivariantly diffusing oriented residue clouds

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:56.960186Z

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Observation 82b0f6d2-c433-43df-b9cc-08609c1eb70a · outbound

This paper cites Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2

Reference 19

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Observation b5e70605-ee63-411c-ae14-e7d4ca416bfc · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 20

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Observation 2219f7c3-dd6e-43a4-8fbf-3f2617bc904d · outbound

This paper cites Flow Matching for Generative Modeling.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Flow Matching for Generative Modeling

Reference 21

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Observation 9c1a2f31-9575-4d8c-aa20-234df134c4ec · outbound

This paper cites Diffusion probabilistic models for 3d point cloud generation.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Diffusion probabilistic models for 3d point cloud generation

Reference 22

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Observation 6f1317d8-f36b-4b18-8dc8-533f80ed410a · outbound

This paper cites Normal distribution on the rotation group so(3).

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Normal distribution on the rotation group so(3)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:56.562543Z

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Observation 5596d618-65ec-4545-afcd-521c0251dbbb · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Pytorch: An imperative style, high-performance deep learning library

Reference 24

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Observation 69e87d3f-d737-49fd-8bf5-aee67658213c · outbound

This paper cites Spectroscopic methods for analysis of protein secondary structure.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Spectroscopic methods for analysis of protein secondary structure

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:56.209013Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e11e42d8-7ee8-4f1a-bdc0-e9c727feae16 · outbound

This paper cites The advent of de novo proteins for cancer immunotherapy.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design The advent of de novo proteins for cancer immunotherapy

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:55.997139Z

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Observation 90cda167-40e2-4e10-bf6f-c8c8c75deacc · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Score-Based Generative Modeling through Stochastic Differential Equations

Reference 27

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Observation 4e5511d3-ff3b-44dd-8b77-be85b6596b47 · outbound

This paper cites Chai-1: Decoding the molecular interactions of life.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Chai-1: Decoding the molecular interactions of life

Reference 28

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Observation b7b1a463-9195-439d-a1d0-f16321b22f75 · outbound

This paper cites Fast and accurate protein structure search with foldseek.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Fast and accurate protein structure search with foldseek

Reference 29

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This paper cites Determination of protein secondary structure.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Determination of protein secondary structure

Reference 30

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Observation 41490598-1f93-4fa9-b0a3-c5c76412899d · outbound

This paper cites The wasserstein distances.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design The wasserstein distances

Reference 31

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Observation ee49ba5c-acdc-476b-aec9-21e0ca5501d5 · outbound

This paper cites De novo design of protein structure and function with rfdiffusion.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design De novo design of protein structure and function with rfdiffusion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:55.221037Z

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Observation a51ed907-be06-4f73-9f4c-5a24cbca0aaf · outbound

This paper cites Design of high-affinity binders to immune modulating receptors for cancer immunotherapy.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Design of high-affinity binders to immune modulating receptors for cancer immunotherapy

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:54.961518Z

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Observation 00bc3cf5-6f28-4da6-834d-4b57b5ef443d · outbound

This paper cites ProteinBench: A Holistic Evaluation of Protein Foundation Models.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design ProteinBench: A Holistic Evaluation of Protein Foundation Models

Reference 34

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Observation 327cc4c1-2eed-4e66-88fd-b94ffae858c2 · outbound

This paper cites Se (3) diffusion model with application to protein backbone generation.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Se (3) diffusion model with application to protein backbone generation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:54.885295Z

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Observation e519bd8c-67b8-4689-be98-0ee1d5c38a9c · outbound

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Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Unresolved cited work

Reference 36

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Observation 328adbe0-d40d-4269-ab89-8deea270aa06 · outbound

This paper cites ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation

Reference 37

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Observation 0cd398b5-d1bd-47c8-bc2d-fb5f5f8a6da5 · outbound

This paper cites Tm-align: a protein structure alignment algorithm based on the tm-score.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Tm-align: a protein structure alignment algorithm based on the tm-score

Reference 38

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Source-reported events for the cited work

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Observation 4db69e62-3213-4225-8d30-f73412491cac · outbound

This paper cites NX i=1 ∥ϵt − ϵθ(xt, t)∥2 # = Et,x0,ϵ.

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design NX i=1 ∥ϵt − ϵθ(xt, t)∥2 # = Et,x0,ϵ

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:45:54.524452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.