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Paper Citation Record · LEDGER

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling

As of 19 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2607.13503.

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

pith.paper-citation-record.v1
2607.13503 v1

Coverage vector

measured 54 of 54 reference resolution

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measured 54 of 54 standing notices

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54 of 54 outbound references displayed

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Outbound references

Observation 466d8429-58c7-4c9f-bef3-f13dee455774 · outbound

This paper cites Gene ontology: tool for the unification of biology.Nature genetics, 25(1):25–29, 2000.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Gene ontology: tool for the unification of biology.Nature genetics, 25(1):25–29, 2000

Reference 1

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Observation ab59846e-64be-40a0-95c7-0df8617bd54c · outbound

This paper cites Self-supervised learning from images with a joint- embedding predictive architecture.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Self-supervised learning from images with a joint- embedding predictive architecture

Reference 2

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Observation 1aaf7238-93c2-4a6f-8f9b-c63d1706aae5 · outbound

This paper cites The enzyme database in 2000.Nucleic acids research, 28(1):304–305, 2000.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling The enzyme database in 2000.Nucleic acids research, 28(1):304–305, 2000

Reference 3

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This paper cites All are worth words: A vit backbone for diffusion models.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling All are worth words: A vit backbone for diffusion models

Reference 4

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Observation 34dc8e58-28a3-4160-b6be-625cd6ffe1c4 · outbound

This paper cites SE(3)-Stochastic Flow Matching for Protein Backbone Generation.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling SE(3)-Stochastic Flow Matching for Protein Backbone Generation

Reference 5

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Observation 127abfb3-86cc-499b-9d58-17f2fdc4d9e0 · outbound

This paper cites Deconstructing Denoising Diffusion Models for Self-Supervised Learning.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 6

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This paper cites Robust deep learning–based protein sequence design using proteinmpnn.Science, 378(6615):49–56, 2022.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Robust deep learning–based protein sequence design using proteinmpnn.Science, 378(6615):49–56, 2022

Reference 7

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Observation e567b871-e26f-475c-9109-c3aa840a8781 · outbound

This paper cites Flow autoencoders are effective protein tokenizers.arXiv preprint arXiv:2510.00351, 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Flow autoencoders are effective protein tokenizers.arXiv preprint arXiv:2510.00351, 2025

Reference 8

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Observation 98fadb6a-8808-4ebc-ac74-a21b2c53a613 · outbound

This paper cites Atomica: Learning universal representations of intermolecular interactions.bioRxiv, pages 2025–04, 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Atomica: Learning universal representations of intermolecular interactions.bioRxiv, pages 2025–04, 2025

Reference 9

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This paper cites Foldtoken: Learning protein language via vector quantization and beyond.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Foldtoken: Learning protein language via vector quantization and beyond

Reference 10

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Observation c72e129b-a1df-4259-bbcb-acdd1bb13a3d · outbound

This paper cites Learning the Language of Protein Structure.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Learning the Language of Protein Structure

Reference 11

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Observation d7be091a-bf04-41f1-836b-f31f953a7413 · outbound

This paper cites La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

Reference 12

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Observation 2b6890e9-33a1-42a1-a81a-7cfcc02c8cab · outbound

This paper cites Simulating 500 million years of evolution with a language model.Science, 387(6736):850–858, 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Simulating 500 million years of evolution with a language model.Science, 387(6736):850–858, 2025

Reference 13

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This paper cites Bilingual language model for protein sequence and structure.NAR Genomics and Bioinformatics, 6(4):lqae150, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Bilingual language model for protein sequence and structure.NAR Genomics and Bioinformatics, 6(4):lqae150, 2024

Reference 14

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Observation 8dfe2d4d-6766-4f27-b7e1-86ad7e6fa487 · outbound

This paper cites Contrastive Representation Learning for 3D Protein Structures.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Contrastive Representation Learning for 3D Protein Structures

Reference 15

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Observation 965efe8a-0887-427a-aed9-a28fea0c9b5c · outbound

This paper cites The coming of age of de novo protein design.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling The coming of age of de novo protein design

Reference 16

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Observation 04dd8849-dcaf-49c5-a3a1-d367b4ada939 · outbound

This paper cites Sequence-augmented se (3)-flow matching for conditional protein generation.Advances in neural information processing systems, 37:33007–33036, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Sequence-augmented se (3)-flow matching for conditional protein generation.Advances in neural information processing systems, 37:33007–33036, 2024

Reference 17

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Observation 85586930-4889-4a61-ad1f-c3780da9ad93 · outbound

This paper cites Illumi- nating protein space with a programmable generative model.Nature, 623(7989):1070–1078, 2023.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Illumi- nating protein space with a programmable generative model.Nature, 623(7989):1070–1078, 2023

Reference 18

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Observation c9981d77-1922-48b7-9b80-23d6a2fbd417 · outbound

This paper cites Evaluating representation learning on the protein structure universe.ArXiv, pages arXiv–2406, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Evaluating representation learning on the protein structure universe.ArXiv, pages arXiv–2406, 2024

Reference 19

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Observation 49201e0e-efd5-4845-b11d-98f41b9de41b · outbound

This paper cites Highly accurate protein structure prediction with alphafold.nature, 596(7873):583–589, 2021.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Highly accurate protein structure prediction with alphafold.nature, 596(7873):583–589, 2021

Reference 20

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This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

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This paper cites Adam: A Method for Stochastic Optimization.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Adam: A Method for Stochastic Optimization

Reference 22

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This paper cites Repa-e: Unlocking vae for end-to-end tuning of latent diffusion transformers.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Repa-e: Unlocking vae for end-to-end tuning of latent diffusion transformers

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Observation 948b7896-22b1-4baf-a2c6-fc35f675e780 · outbound

This paper cites Your diffusion model is secretly a zero-shot classifier.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Your diffusion model is secretly a zero-shot classifier

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

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2

Reference 25

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This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130, 2023.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130, 2023

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This paper cites Structure Language Models for Protein Conformation Generation.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Structure Language Models for Protein Conformation Generation

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This paper cites Assessing generative model coverage of protein structures with shapes.Cell Systems, 16(8), 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Assessing generative model coverage of protein structures with shapes.Cell Systems, 16(8), 2025

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This paper cites Conditional protein structure generation with protpardelle-1c.bioRxiv, 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Conditional protein structure generation with protpardelle-1c.bioRxiv, 2025

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This paper cites Bioto- ken and biofm–biologically-informed tokenization enables accurate and efficient genomic foundation models.bioRxiv, pages 2025–03, 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Bioto- ken and biofm–biologically-informed tokenization enables accurate and efficient genomic foundation models.bioRxiv, pages 2025–03, 2025

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This paper cites Machine learning for functional protein design.Nature biotechnology, 42(2):216–228, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Machine learning for functional protein design.Nature biotechnology, 42(2):216–228, 2024

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This paper cites Gcc: Graph contrastive coding for graph neural network pre-training.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Gcc: Graph contrastive coding for graph neural network pre-training

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This paper cites Instructplm: Aligning protein language models to follow protein structure instructions.bioRxiv, pages 2024–04, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Instructplm: Aligning protein language models to follow protein structure instructions.bioRxiv, pages 2024–04, 2024

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This paper cites Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.Proceedings of the National Academy of Sciences, 116(28):13996–14001, 2019.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.Proceedings of the National Academy of Sciences, 116(28):13996–14001, 2019

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This paper cites Fine-tuning protein language models boosts predictions across diverse tasks.Nature Communications, 15(1):7407, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Fine-tuning protein language models boosts predictions across diverse tasks.Nature Communications, 15(1):7407, 2024

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Observation ba7e65f2-dc4c-4b2a-ac7a-064800486158 · outbound

This paper cites Cath: increased structural coverage of functional space.Nucleic acids research, 49(D1):D266–D273, 2021.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Cath: increased structural coverage of functional space.Nucleic acids research, 49(D1):D266–D273, 2021

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source=pdf_text observed=2026-08-02T05:05:23.544160Z digest=sha256:a907d27bb27ee0dd296c8b5f77fd9c8c4dacc6238ff1f64befa9d952d50c5012

Observation b1b5a8df-535c-458d-b84e-20d96ff9b66b · outbound

This paper cites Saprot: Protein language modeling with structure-aware vocabulary.BioRxiv, pages 2023–10, 2023.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Saprot: Protein language modeling with structure-aware vocabulary.BioRxiv, pages 2023–10, 2023

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source=pdf_text observed=2026-08-02T05:05:23.607055Z digest=sha256:e915ab516c4d19ca799f2406ba5eef421aa7448679cbb69629bd730aa673d925

Observation 8c0b9ed8-fc9d-4eb8-a292-fa1e8fd83076 · outbound

This paper cites Scaling text-to-image diffusion transformers with representation autoencoders.arXiv preprint arXiv:2601.16208, 2026.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Scaling text-to-image diffusion transformers with representation autoencoders.arXiv preprint arXiv:2601.16208, 2026

Reference 38

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no resolver link, observed 2026-08-02T05:05:23.685278Z

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source=pdf_text observed=2026-08-02T05:05:23.685278Z digest=sha256:de52a56faf1e27ddc18868a723cf5a76756352caa1b0231f91887a6d73d165e6

Observation 138a6ebc-3431-4d65-ba25-96d19586852b · outbound

This paper cites Neural discrete representation learning.Advances in neural information processing systems, 30, 2017.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

Reference 39

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no resolver link, observed 2026-08-02T05:05:23.777028Z

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source=pdf_text observed=2026-08-02T05:05:23.777028Z digest=sha256:48ceff7a3c404b3d2b7ce96bc178a838c154958f090f058ae66116fd084638d6

Observation 543f9bcf-8e91-4565-b72c-fdff4c53d2c2 · outbound

This paper cites Foldseek: fast and accurate protein structure search.Biorxiv, pages 2022–02, 2022.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Foldseek: fast and accurate protein structure search.Biorxiv, pages 2022–02, 2022

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no resolver link, observed 2026-08-02T05:05:23.832275Z

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source=pdf_text observed=2026-08-02T05:05:23.832275Z digest=sha256:09b1875a6a70481891c849ec7f20e0d94571863a1e187c0e1cca45d734b1dabb

Observation 58d83bc4-8f9f-47cd-bc97-a6efceef9360 · outbound

This paper cites Fast and accurate protein structure search with foldseek.Nature biotechnology, 42(2):243–246, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Fast and accurate protein structure search with foldseek.Nature biotechnology, 42(2):243–246, 2024

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no resolver link, observed 2026-08-02T05:05:23.883056Z

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source=pdf_text observed=2026-08-02T05:05:23.883056Z digest=sha256:0bc7021a836754751bdf6bfe39d00014c0ad0f3f0180a641c4d9c92283b51049

Observation 7a44011d-115b-402e-8d38-dd1197e8e182 · outbound

This paper cites DPLM-2: A Multimodal Diffusion Protein Language Model.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling DPLM-2: A Multimodal Diffusion Protein Language Model

Reference 42

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no resolver link, observed 2026-08-02T05:05:23.910752Z

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source=pdf_text observed=2026-08-02T05:05:23.910752Z digest=sha256:368a1b04e5fccca661797393d6e711530d44b3bc5edaccda5ed58b9b01ebbc32

Observation 88c38eae-6df7-4ea5-8a42-de1824a49704 · outbound

This paper cites De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100, 2023.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100, 2023

Reference 43

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no resolver link, observed 2026-08-02T05:05:23.984721Z

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source=pdf_text observed=2026-08-02T05:05:23.984721Z digest=sha256:09c80a7c6b51b3d2466f36658856155ebb9ab37712065537edad31448352cb19

Observation c89af21c-6936-4aaf-a354-60f50c6d8992 · outbound

This paper cites Sidechain conditioning and modeling for full-atom protein sequence design with fampnn.Proceedings of machine learning research, 267:66746, 2025.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Sidechain conditioning and modeling for full-atom protein sequence design with fampnn.Proceedings of machine learning research, 267:66746, 2025

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no resolver link, observed 2026-08-02T05:05:24.028138Z

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source=pdf_text observed=2026-08-02T05:05:24.028138Z digest=sha256:2a2538eef44c3064c19e35064f819e5f170e99df12d087556205ec5e1246bdd4

Observation e619fc5a-fe84-4c0c-8b84-296b3510da41 · outbound

This paper cites Generative artificial intelligence for de novo protein design.Current Opinion in Structural Biology, 86:102794, 2024.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Generative artificial intelligence for de novo protein design.Current Opinion in Structural Biology, 86:102794, 2024

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no resolver link, observed 2026-08-02T05:05:24.055835Z

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source=pdf_text observed=2026-08-02T05:05:24.055835Z digest=sha256:21ef05a79e63eae50c509e89b46c37d048d3f2346c3b7a89a17b61eabade027d

Observation dfa9d79b-e84c-4b54-a474-f72e3788480f · outbound

This paper cites Denoising diffusion autoencoders are unified self-supervised learners.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Denoising diffusion autoencoders are unified self-supervised learners

Reference 46

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no resolver link, observed 2026-08-02T05:05:24.094418Z

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source=pdf_text observed=2026-08-02T05:05:24.094418Z digest=sha256:169232b35568cb9fead203e4835b3dc4697ed73c4a114f08eb3d246685382264

Observation 48002b91-2552-462d-8ed0-9ee46cf2792c · outbound

This paper cites Fast protein backbone generation with SE(3) flow matching.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Fast protein backbone generation with SE(3) flow matching

Reference 47

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no resolver link, observed 2026-08-02T05:05:24.123759Z

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source=pdf_text observed=2026-08-02T05:05:24.123759Z digest=sha256:ad9be6d68c4ca6b9da5a67b69784d1d0b27f9609f89a75709f476ff8ec80a554

Observation 6e23f594-b4cb-467d-8d0f-c716263090c0 · outbound

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

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling SE(3) diffusion model with application to protein backbone generation

Reference 48

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no resolver link, observed 2026-08-02T05:05:24.151157Z

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source=pdf_text observed=2026-08-02T05:05:24.151157Z digest=sha256:7ba7325810d5ba7f29e105184fb2401b7d11fb8b02fc00a8a89d3df709310157

Observation 10f0ae0f-81a4-4ac7-8d81-79e9568a8fa4 · outbound

This paper cites Graph contrastive learning with augmentations.Advances in neural information processing systems, 33:5812–5823, 2020.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Graph contrastive learning with augmentations.Advances in neural information processing systems, 33:5812–5823, 2020

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no resolver link, observed 2026-08-02T05:05:24.184447Z

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source=pdf_text observed=2026-08-02T05:05:24.184447Z digest=sha256:33c0c7127e9861c775f59ccbadbdd04cce1598bd84a842f38028f6182ee3f223

Observation 0334786a-7951-46df-932b-6ec7ce1465b4 · outbound

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

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 50

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no resolver link, observed 2026-08-02T05:05:24.215447Z

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source=pdf_text observed=2026-08-02T05:05:24.215447Z digest=sha256:4f3c3627c2bc06172c5346395ab00f0f4f0993d74f24940365952611d0a62f94

Observation 4ad55856-927d-4f9d-9dd5-d0f97e3637e9 · outbound

This paper cites Enzyme function prediction using contrastive learning.Science, 379(6639):1358–1363, 2023.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Enzyme function prediction using contrastive learning.Science, 379(6639):1358–1363, 2023

Reference 51

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no resolver link, observed 2026-08-02T05:05:24.244993Z

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source=pdf_text observed=2026-08-02T05:05:24.244993Z digest=sha256:52f8d2fbb31420b10ef7d1d5a2469a22eba3a97e8544a38d885fff7697296780

Observation 448fc75b-59e6-49c8-a484-db78675799ca · outbound

This paper cites Protein Representation Learning by Geometric Structure Pretraining.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Protein Representation Learning by Geometric Structure Pretraining

Reference 52

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no resolver link, observed 2026-08-02T05:05:24.273857Z

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source=pdf_text observed=2026-08-02T05:05:24.273857Z digest=sha256:e8258da29fdbea5fe913720158693927facae741682fb4203db08b8b5288814b

Observation 68f119a5-d34d-4c93-9e71-7307d63e2116 · outbound

This paper cites Diffusion Transformers with Representation Autoencoders.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Diffusion Transformers with Representation Autoencoders

Reference 53

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no resolver link, observed 2026-08-02T05:05:24.329127Z

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source=pdf_text observed=2026-08-02T05:05:24.329127Z digest=sha256:62c5121b3b8fcd629924cbb960a481155c54d94009a414741151bda58d613b13

Observation 6255446d-55dc-4be1-ab70-9c1de8bd83c7 · outbound

This paper cites MotifBench: A standardized protein design benchmark for motif-scaffolding problems.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling MotifBench: A standardized protein design benchmark for motif-scaffolding problems

Reference 54

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source=pdf_text observed=2026-08-02T05:05:24.368195Z digest=sha256:6c95cb4263beae4c4a21e69ad0ee3175c240f0086c5832b91af152427d76167d

Pith citing papers

No inbound Pith citation observations are available.