ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation | Paper Citation Record

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This paper cites Uncovering protein function: from classification to complexes.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Uncovering protein function: from classification to complexes

Reference 1

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This paper cites Sensing the shape of functional proteins with topology.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Sensing the shape of functional proteins with topology

Reference 2

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

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation The coming of age of de novo protein design

Reference 3

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This paper cites Recent advances in de novo protein design: principles, methods, and applications.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Recent advances in de novo protein design: principles, methods, and applications

Reference 4

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This paper cites Leveraging Deep Generative Model For Computational Protein Design And Optimization.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Leveraging Deep Generative Model For Computational Protein Design And Optimization

Reference 5

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This paper cites A comprehensive review and comparison of existing com- putational methods for protein function prediction.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation A comprehensive review and comparison of existing com- putational methods for protein function prediction

Reference 6

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This paper cites Generative adversarial nets.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative adversarial nets

Reference 7

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This paper cites Auto-Encoding Variational Bayes.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Auto-Encoding Variational Bayes

Reference 8

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This paper cites Variational inference with normalizing flows.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Variational inference with normalizing flows

Reference 9

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This paper cites Generative modeling for protein structures.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative modeling for protein structures

Reference 10

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Observation 4d219cfd-0cc1-4212-a809-5755e83ccfca · outbound

This paper cites ProteinVAE: Variational autoencoder for trans- lational protein design.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation ProteinVAE: Variational autoencoder for trans- lational protein design

Reference 11

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Observation 91e7c84f-92c9-4cab-98ea-56289a562d42 · outbound

This paper cites ProtTrans: Toward understanding the language of life through self-supervised learning.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation ProtTrans: Toward understanding the language of life through self-supervised learning

Reference 12

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This paper cites Prot-VAE: Protein transformer variational au- toencoder for functional protein design.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Prot-VAE: Protein transformer variational au- toencoder for functional protein design

Reference 13

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This paper cites Expanding functional protein sequence spaces using generative adversarial networks.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Expanding functional protein sequence spaces using generative adversarial networks

Reference 14

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This paper cites Deep generative modeling for protein design.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Deep generative modeling for protein design

Reference 15

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Observation 1f7fb4f0-859c-4082-b04f-3fe27b5f3b07 · outbound

This paper cites Computational protein design with deep learning neural networks.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Computational protein design with deep learning neural networks

Reference 16

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Observation d2079f9f-d85b-4ed1-9245-af07497374ab · outbound

This paper cites DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet

Reference 17

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Observation 9af03f05-00a4-4ef8-85e6-e8011b67f2cd · outbound

This paper cites De novo protein design by deep network hallucination.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation De novo protein design by deep network hallucination

Reference 18

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Observation 1c83352c-9268-493d-9153-eafbcdb63000 · outbound

This paper cites Robust deep learning-based protein sequence design using Pro- teinMPNN.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Robust deep learning-based protein sequence design using Pro- teinMPNN

Reference 19

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This paper cites ProtGPT2 is a deep unsupervised language model for protein design.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation ProtGPT2 is a deep unsupervised language model for protein design

Reference 20

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Observation ebe39b1e-4551-4af3-b0e2-650d50eb0b71 · outbound

This paper cites Score-based gen- erative modeling through stochastic differential equations.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Score-based gen- erative modeling through stochastic differential equations

Reference 21

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Observation ec8e1fec-8c5a-421d-8dd4-13d7632864c9 · outbound

This paper cites Denoising diffusion probabilistic models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Denoising diffusion probabilistic models

Reference 22

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This paper cites Generative modeling by estimating gradients of the data distribu- tion.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative modeling by estimating gradients of the data distribu- tion

Reference 23

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Observation f6821627-3e51-4ecd-9bc2-fbb6e0d7e1b3 · outbound

This paper cites Score-based generative models with L´ evy processes.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Score-based generative models with L´ evy processes

Reference 24

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This paper cites Annealed fractional L´ evy–It¯ o diffusion models for protein generation.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Annealed fractional L´ evy–It¯ o diffusion models for protein generation

Reference 25

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This paper cites Generative Fractional Diffusion Models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative Fractional Diffusion Models

Reference 26

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This paper cites Affine representations of fractional processes with appli- cations in mathematical finance.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Affine representations of fractional processes with appli- cations in mathematical finance

Reference 27

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This paper cites Variational inference for SDEs driven by fractional noise.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Variational inference for SDEs driven by fractional noise

Reference 28

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Observation f7385940-8d2b-4fbb-b523-f231e589c46a · outbound

This paper cites Improved denoising diffusion probabilistic models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Improved denoising diffusion probabilistic models

Reference 29

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This paper cites Reverse-time diffusion equation models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Reverse-time diffusion equation models

Reference 30

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This paper cites Numerical solution of stochastic differential equations.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Numerical solution of stochastic differential equations

Reference 31

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This paper cites Neural ordinary differential equations.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Neural ordinary differential equations

Reference 32

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This paper cites Basic local alignment search tool.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Basic local alignment search tool

Reference 33

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This paper cites DeepRED: automated protein function prediction with multi-task feed-forward deep neural networks.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation DeepRED: automated protein function prediction with multi-task feed-forward deep neural networks

Reference 34

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Observation 00bf258c-d74c-4a16-a0bb-f84f4acdd41b · outbound

This paper cites DeepGOPlus: improved protein function predic- tion from sequence.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation DeepGOPlus: improved protein function predic- tion from sequence

Reference 35

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This paper cites Structure-based protein function prediction using graph convolutional networks.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Structure-based protein function prediction using graph convolutional networks

Reference 36

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This paper cites Long short-term memory.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Long short-term memory

Reference 37

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This paper cites Hierarchical graph transformer with contrastive learning for protein function prediction.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Hierarchical graph transformer with contrastive learning for protein function prediction

Reference 38

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Observation f6f680c4-790e-4b7c-ae63-e2aa13a20a21 · outbound

This paper cites GeneMANIA: a real-time multiple as- sociation network integration algorithm for predicting gene function.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation GeneMANIA: a real-time multiple as- sociation network integration algorithm for predicting gene function

Reference 39

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This paper cites DeepNF: deep network fusion for protein function prediction.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation DeepNF: deep network fusion for protein function prediction

Reference 40

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This paper cites The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Reference 41

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This paper cites A comprehensive review and comparison of ex- isting computational methods for protein function prediction.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation A comprehensive review and comparison of ex- isting computational methods for protein function prediction

Reference 42

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This paper cites Generative models for pro- tein sequence modeling: recent advances and future directions.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative models for pro- tein sequence modeling: recent advances and future directions

Reference 43

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Observation f476dcad-aea3-4b30-a22f-5cf0993d2a1b · outbound

This paper cites Scaffolding protein functional sites using deep learning.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Scaffolding protein functional sites using deep learning

Reference 44

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Observation d54da6ef-af74-4280-b5b6-3c3e9332111f · outbound

This paper cites Score-based generative modeling for de novo protein design.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Score-based generative modeling for de novo protein design

Reference 45

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Observation b2a0cd0f-ca8c-49de-912b-d17aaa16fcf2 · outbound

This paper cites The Protein Data Bank: a computer-based archival file for macromolecular structures.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation The Protein Data Bank: a computer-based archival file for macromolecular structures

Reference 46

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This paper cites Generative modeling for protein structures.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative modeling for protein structures

Reference 47

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Observation c178bdb9-756b-47db-a40f-b4cb2f13941f · outbound

This paper cites Sampling realistic protein conformations using local structural bias.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Sampling realistic protein conformations using local structural bias

Reference 48

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Observation 335c1a50-68e8-4cd0-99bd-55ffe6bbdea7 · outbound

This paper cites A generative, probabilistic model of local protein structure.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation A generative, probabilistic model of local protein structure

Reference 49

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Observation e7aff876-b6fa-4916-bef3-2a563c90d2fe · outbound

This paper cites Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling

Reference 50

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Observation 5c180004-8d9b-47ce-b8f4-2e64171e9da8 · outbound

This paper cites Protein structure prediction using Rosetta.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Protein structure prediction using Rosetta

Reference 51

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Observation 94104c62-d2ae-4e7c-9510-2d59d6e6b66d · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 52

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This paper cites Fully differentiable full-atom protein backbone generation.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Fully differentiable full-atom protein backbone generation

Reference 53

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This paper cites Fractional Brownian motion in a nutshell.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Fractional Brownian motion in a nutshell

Reference 54

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This paper cites Estimation of non-normalized statistical models by score matching.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Estimation of non-normalized statistical models by score matching

Reference 55

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This paper cites Estimating the Hessian by backpropagating cur- vature.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Estimating the Hessian by backpropagating cur- vature

Reference 56

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This paper cites Sliced score matching: A scalable approach to density and score estimation.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Sliced score matching: A scalable approach to density and score estimation

Reference 57

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This paper cites A connection between score matching and denoising autoencoders.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation A connection between score matching and denoising autoencoders

Reference 58

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This paper cites Generative modeling by estimating gradients of the data distribu- tion.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Generative modeling by estimating gradients of the data distribu- tion

Reference 59

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This paper cites Improved techniques for training score-based generative models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Improved techniques for training score-based generative models

Reference 60

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This paper cites Denoising diffusion implicit models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Denoising diffusion implicit models

Reference 61

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This paper cites Numerical solution of stochastic differential equations.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Numerical solution of stochastic differential equations

Reference 62

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Observation 84d18e92-9789-434c-8c4b-97f2fd9c3acd · outbound

This paper cites Approximate integration of stochastic differential equations.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Approximate integration of stochastic differential equations

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:20:57.360397Z

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ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Unresolved cited work

Reference 64

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Observation 04654977-a975-4b5c-b2dd-7eb4d2cf9731 · outbound

This paper cites The probability flow ODE is provably fast.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation The probability flow ODE is provably fast

Reference 65

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This paper cites Numerical continuation methods: an introduction.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Numerical continuation methods: an introduction

Reference 66

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Observation 76fbfc4b-d336-49ac-944a-38cd95424248 · outbound

This paper cites Fast Sampling of Diffusion Models with Exponential Integrator.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Fast Sampling of Diffusion Models with Exponential Integrator

Reference 67

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This paper cites DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Reference 68

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Observation f94b36bf-d2fa-4213-9973-7970d821e3b3 · outbound

This paper cites U-Net: convolutional networks for biomedical im- age segmentation.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation U-Net: convolutional networks for biomedical im- age segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:20:57.323226Z

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Observation c6f01347-d989-4221-aad9-e48100c1c312 · outbound

This paper cites Variational diffusion models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Variational diffusion models

Reference 70

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Observation f1629177-13d1-4217-b316-ecf2c85ed75a · outbound

This paper cites Improved precision and recall metric for assessing generative models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Improved precision and recall metric for assessing generative models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:20:57.296844Z

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This paper cites Assessing generative models via precision and recall.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Assessing generative models via precision and recall

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:20:57.310689Z

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Observation 4be01c5e-2c3b-4b7c-886a-11c0481d37ca · outbound

This paper cites Is noise conditioning necessary for denoising generative models? arXiv preprint.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Is noise conditioning necessary for denoising generative models? arXiv preprint

Reference 73

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Observation fe4dcf93-64ef-4e19-9145-1fa3da0133da · outbound

This paper cites Reliable fidelity and diversity metrics for generative models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Reliable fidelity and diversity metrics for generative models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:20:57.281965Z

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Observation 56ace3fc-6777-41d0-8e2e-42ec3236e39c · outbound

This paper cites Score nor- malization for a faster diffusion exponential integrator sampler.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Score nor- malization for a faster diffusion exponential integrator sampler

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:20:57.268881Z

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Observation 7146f517-bb86-4363-8068-90a3cc16c306 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 76

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ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation Unresolved cited work

Reference 2024

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