Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings | Paper Citation Record

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This paper cites Molecular dynamics and protein function.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Molecular dynamics and protein function

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This paper cites Dynamic personalities of proteins.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Dynamic personalities of proteins

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This paper cites Highly accurate protein structure prediction with AlphaFold.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Highly accurate protein structure prediction with AlphaFold

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This paper cites Accurate pre- diction of protein structures and interactions using a three-track neural network.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Accurate pre- diction of protein structures and interactions using a three-track neural network

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

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Evolutionary-scale prediction of atomic-level protein structure with a language model

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

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Accurate structure prediction of biomolecular interactions with alphafold 3

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This paper cites Boltz-1: Democratizing biomolecular interaction modeling.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Boltz-1: Democratizing biomolecular interaction modeling

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This paper cites The role of dynamic conformational ensembles in biomolecular recognition.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings The role of dynamic conformational ensembles in biomolecular recognition

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This paper cites Implications of protein flexibility for drug discovery.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Implications of protein flexibility for drug discovery

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This paper cites Computational design of g protein- coupled receptor allosteric signal transductions.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Computational design of g protein- coupled receptor allosteric signal transductions

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This paper cites Computational design of highly signalling-active membrane receptors through solvent-mediated allosteric networks.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Computational design of highly signalling-active membrane receptors through solvent-mediated allosteric networks

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This paper cites Side-chain flex- ibility in proteins upon ligand binding.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Side-chain flex- ibility in proteins upon ligand binding

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This paper cites Deep learning approaches for confor- mational flexibility and switching properties in protein design.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Deep learning approaches for confor- mational flexibility and switching properties in protein design

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

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100, 2023

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This paper cites Protein structure generation via folding diffusion.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Protein structure generation via folding diffusion

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This paper cites Pro- teina: Scaling flow-based protein structure generative models.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Pro- teina: Scaling flow-based protein structure generative models

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This paper cites An all-atom protein generative model.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings An all-atom protein generative model

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This paper cites Illumi- nating protein space with a programmable generative model.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Illumi- nating protein space with a programmable generative model

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This paper cites Alphafold2-rave: From sequence to boltzmann ranking.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Alphafold2-rave: From sequence to boltzmann ranking

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This paper cites Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling

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This paper cites AlphaFold Meets Flow Matching for Generating Protein Ensembles.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings AlphaFold Meets Flow Matching for Generating Protein Ensembles

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

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This paper cites Predicting equilibrium distributions for molecular systems with deep learning.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Predicting equilibrium distributions for molecular systems with deep learning

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This paper cites A latent diffusion model for protein structure generation.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings A latent diffusion model for protein structure generation

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This paper cites Lu, Wilson Yan, Vladimir Gligorijevic, Kyunghyun Cho, Richard Bonneau, Kevin K.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Lu, Wilson Yan, Vladimir Gligorijevic, Kyunghyun Cho, Richard Bonneau, Kevin K

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This paper cites Transferable deep generative modeling of intrinsically disordered protein conformations.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Transferable deep generative modeling of intrinsically disordered protein conformations

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This paper cites Protein Conformation Generation via Force-Guided SE(3) Diffusion Models.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Protein Conformation Generation via Force-Guided SE(3) Diffusion Models

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

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This paper cites Latorraca, A.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Latorraca, A

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This paper cites G protein-coupled receptors (gpcrs): advances in structures, mechanisms and drug discovery.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings G protein-coupled receptors (gpcrs): advances in structures, mechanisms and drug discovery

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This paper cites What are the current trends in g protein-coupled receptor targeted drug discovery? Expert Opinion on Drug Discovery , 18(8):815–820, 2023.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings What are the current trends in g protein-coupled receptor targeted drug discovery? Expert Opinion on Drug Discovery , 18(8):815–820, 2023

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings G protein-coupled receptors: structure- and function- based drug discovery

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Drugbank 5.0: a major update to the drugbank database for 2018

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Structure and dynamics of gpcr signaling complexes

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Monod, J

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Gpcr dynamics: structures in motion

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis

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This paper cites Biased receptor signaling in drug discovery.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Biased receptor signaling in drug discovery

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Goupil, S

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This paper cites Jeffrey Conn, Arthur Christopoulos, and Craig W.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Jeffrey Conn, Arthur Christopoulos, and Craig W

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This paper cites Deep learning dynamic allostery of G-Protein- Coupled receptors.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Deep learning dynamic allostery of G-Protein- Coupled receptors

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This paper cites Can molecular dynamics simulations improve the structural accuracy and virtual screening perfor- mance of gpcr models? PLOS Computational Biology, 17(5):e1008936, 2021.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Can molecular dynamics simulations improve the structural accuracy and virtual screening perfor- mance of gpcr models? PLOS Computational Biology, 17(5):e1008936, 2021

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This paper cites Eric Xu, and Xi Cheng.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Eric Xu, and Xi Cheng

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This paper cites Gpcrmd uncovers the dynamics of the 3d-gpcrome.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Gpcrmd uncovers the dynamics of the 3d-gpcrome

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This paper cites Gpcr molecular dynamics forecasting using recur- rent neural networks.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Gpcr molecular dynamics forecasting using recur- rent neural networks

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correction dated 2024-04-30. Source: crossref record 10.1038/s41598-024-60566-w->10.1038/s41598-023-48346-4:correction, observed 2026-07-11T02:59:39.651622+00:00. This notice travels one citation hop only.

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This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Convolutional neural networks on graphs with fast localized spectral filtering

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This paper cites Denoising diffusion probabilistic models.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Denoising diffusion probabilistic models

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This paper cites Flow Matching for Generative Modeling.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Flow Matching for Generative Modeling

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This paper cites Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Ig-vae: Generative modeling of protein structure by direct 3d coordinate generation

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This paper cites FlowPacker: Protein side-chain packing with torsional flow matching.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings FlowPacker: Protein side-chain packing with torsional flow matching

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This paper cites Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

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Observation ec76b874-2ff1-4cd5-b273-5b6d89d94a7c · outbound

This paper cites Protein en- semble generation through variational autoencoder latent space sampling.Journal of Chemical Theory and Computation, 20(7):2689–2695, 2024.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Protein en- semble generation through variational autoencoder latent space sampling.Journal of Chemical Theory and Computation, 20(7):2689–2695, 2024

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This paper cites P2dflow: A protein ensemble generative model with SE(3) flow matching.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings P2dflow: A protein ensemble generative model with SE(3) flow matching

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Observation 0d0c3f5e-bdda-4cd1-a678-b291250e7e34 · outbound

This paper cites Generative Modeling of Molecular Dynamics Trajectories.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Generative Modeling of Molecular Dynamics Trajectories

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.454111Z

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Observation ee40ba9a-2aaa-41fb-9c86-b69c2c8912f4 · outbound

This paper cites A solution for the best rotation to relate two sets of vectors.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings A solution for the best rotation to relate two sets of vectors

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.162333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e2105dc-8c59-426d-a2ee-9661ca5261f1 · outbound

This paper cites Universal activation index for class a gpcrs.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Universal activation index for class a gpcrs

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.141422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8f54ad14-d9e5-48c7-a4ed-af927a55f1ad · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.472694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c073df4-5458-4b00-bec3-f09eb50b72ca · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Adam: A Method for Stochastic Optimization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.478300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0010f447-2db5-4438-b6b2-b4a503903b0a · outbound

This paper cites Diffpie: Guiding deep generative models to explore protein conformations under external interactions.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Diffpie: Guiding deep generative models to explore protein conformations under external interactions

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.120204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4052e2c-74f2-440f-890d-cfc7c54c1155 · outbound

This paper cites Physdock: A physics-guided all-atom diffusion model for protein-ligand complex prediction.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Physdock: A physics-guided all-atom diffusion model for protein-ligand complex prediction

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.098775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c7de2799-23c0-4b10-bbff-7c4df4f8349b · outbound

This paper cites PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.495499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f58fe570-1e89-4d71-a447-a6e29aca803b · outbound

This paper cites Atomica: Learning universal representations of intermolecular interactions.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Atomica: Learning universal representations of intermolecular interactions

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.080374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4a22f3de-ebde-4c33-ae93-078e0e2e0c04 · outbound

This paper cites All-atom diffusion transformers: Unified generative modelling of molecules and materials.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings All-atom diffusion transformers: Unified generative modelling of molecules and materials

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.060111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a27da42c-de7b-4ea7-bcb8-375fd365bfd4 · outbound

This paper cites P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:17:31.119817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4d4786e5-2603-4c25-880b-cd17765eab50 · outbound

This paper cites Structure of the d2 dopamine receptor bound to the atypical antipsychotic drug risperidone.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Structure of the d2 dopamine receptor bound to the atypical antipsychotic drug risperidone

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.042020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 64cea2c9-88bb-4ad0-93fb-62d671a3ceff · outbound

This paper cites Rosettaremodel: a generalized framework for flexible backbone protein design.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Rosettaremodel: a generalized framework for flexible backbone protein design

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.024293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b207e2c3-a95a-4ff5-bef3-570596e9dfcb · outbound

This paper cites Charmm-gui membrane builder toward realistic biological membrane simulations, 2014.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Charmm-gui membrane builder toward realistic biological membrane simulations, 2014

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:32.004389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca65bbb4-1610-4e8c-a89f-51122a473fa6 · outbound

This paper cites Structure and dynamics of the tip3p, spc, and spc/e water models at 298 k.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Structure and dynamics of the tip3p, spc, and spc/e water models at 298 k

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.984999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d284ada2-2543-4f6f-ba76-6579eca64da2 · outbound

This paper cites Charmm36m: an improved force field for folded and intrinsically disordered proteins.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Charmm36m: an improved force field for folded and intrinsically disordered proteins

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.963854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ea799ae0-f647-4c28-8268-8b1da9d71e83 · outbound

This paper cites Gromacs: High performance molecular simulations through multi- level parallelism from laptops to supercomputers.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Gromacs: High performance molecular simulations through multi- level parallelism from laptops to supercomputers

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.942748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c2b68d5c-161d-480c-9a32-cef4c149e1bc · outbound

This paper cites Canonical sampling through veloc- ity rescaling.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Canonical sampling through veloc- ity rescaling

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.923488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a77bf663-d0f7-44b0-be06-ff292993801d · outbound

This paper cites Pressure control using stochastic cell rescaling.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Pressure control using stochastic cell rescaling

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.563006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 280623ce-bc86-4a68-8d1b-c1bad45c5fc0 · outbound

This paper cites Lincs: A linear constraint solver for molecular simulations.Journal of computational chemistry, 18(12):1463– 1472, 1997.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Lincs: A linear constraint solver for molecular simulations.Journal of computational chemistry, 18(12):1463– 1472, 1997

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.893919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f5b978a7-7e6a-4aa6-b101-c76c01911225 · outbound

This paper cites A smooth particle mesh ewald method.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings A smooth particle mesh ewald method

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.875821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40ae7af0-18bf-47fa-92a1-4b3943cffab3 · outbound

This paper cites lddt: a local superposition-free score for comparing protein structures and models using distance difference tests.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings lddt: a local superposition-free score for comparing protein structures and models using distance difference tests

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.856826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dcb09727-2e9a-4cb7-959c-a2a785b643e5 · outbound

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

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Tm-align: a protein structure alignment algorithm based on the tm-score

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.586198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0d3a3a1-574e-4e85-97b8-c9c83e019aed · outbound

This paper cites Biopython: freely available python tools for computational molecular biology and bioinformatics.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Biopython: freely available python tools for computational molecular biology and bioinformatics

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.827798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6289ac57-31da-4c11-8fa5-cfbd20b6619f · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cour- napeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St ´efan J.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Oliphant, Matt Haberland, Tyler Reddy, David Cour- napeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St ´efan J

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.598505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f53e598b-bef1-4080-9ec2-7ae1b30d0a80 · outbound

This paper cites Boosting diffusion models with moving average sampling in frequency domain.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Boosting diffusion models with moving average sampling in frequency domain

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.604017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a6490fa-2098-4c16-a215-14ca60ac825c · outbound

This paper cites Towards the systematic reporting of the energy and carbon footprints of machine learning.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Towards the systematic reporting of the energy and carbon footprints of machine learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T19:17:30.610206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81cd9a7c-c700-4cb7-ad97-17e1422f1900 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.761196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 64c85d0a-72d8-4db6-807e-4eb00812e3f7 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.741313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b1803aa9-9098-4b5b-9b39-d47459b59f0e · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.724160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84d4d100-086d-4958-8599-0dedc94d1ea8 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.704394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 65f1c758-374d-4af9-883e-f2f53c1d162c · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.685300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bd15b988-dc40-4338-8eec-f3f2644403d0 · outbound

This paper cites This operation pools across all N atoms for each sample in the batch: h(b) global =Pglobal(Z(b))∈ Rdp wheredp =H·W is the dimension of the pooled global context vector.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings This operation pools across all N atoms for each sample in the batch: h(b) global =Pglobal(Z(b))∈ Rdp wheredp =H·W is the dimension of the pooled global context vector

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.666746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 63208924-ca7a-4613-9607-a4aaeb1c0ec7 · outbound

This paper cites For a batch, this is Hglobal ex∈ RB×N×dp.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings For a batch, this is Hglobal ex∈ RB×N×dp

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.649779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 01b5c4d2-cfc2-42a3-8317-ab22e08a8429 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.614802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4a1d0eca-bb75-4300-9c92-65b1ad0230d0 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.597734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dcb11fda-bdc6-4ca3-add6-5cae13f19f3a · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.578810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 54a7abab-9c1b-448b-8be6-f0581fc535e9 · outbound

This paper cites For a batch, this is Hbb∈ RB×dp,bb.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings For a batch, this is Hbb∈ RB×dp,bb

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.560576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 789f1791-868c-4dec-bd53-730176c0fada · outbound

This paper cites C(b) bb is used directly.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings C(b) bb is used directly

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.543855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 79a53d35-b914-4693-9db2-5854a0df8a4d · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.526538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e011cdd7-9ab9-4b22-aeee-d434bd80a691 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.509534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b6758b45-eb54-4a2a-b631-d2a4f2505339 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 99

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.491841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9fcfa95f-70d5-42ac-9407-c67520a79f00 · outbound

This paper cites For a batch, this results in Hsc∈ RB×dp,sc.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings For a batch, this results in Hsc∈ RB×dp,sc

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.473486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d821feeb-00f4-4b39-858b-2bfbba82cf3c · outbound

This paper cites The construction varies based on the arch type: • Let X(b) pred, bb flat∈ RNbb·3 be the flattened predicted backbone coordinates for sample b.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings The construction varies based on the arch type: • Let X(b) pred, bb flat∈ RNbb·3 be the flattened predicted backbone coordinates for sample b

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:17:31.452934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d86ebf8e-7af5-4286-a917-f0742de3d8d5 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 102

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.434082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0e4d0138-c761-4521-896e-1c7e59a6bf24 · outbound

This paper cites an unresolved cited work.

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings Unresolved cited work

Reference 103

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:17:31.417797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Reference resolution

Outbound references

Pith citing papers