Paper Citation Record · LEDGER

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2507.13950.

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

pith.paper-citation-record.v1
2507.13950 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:23:03.910831Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d86f1f35-4154-46f1-9fc4-1c0021f2863a · outbound

This paper cites Enhanced sampling in molecular dynamics.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Enhanced sampling in molecular dynamics

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.166709Z

Source-reported events for the cited work

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Observation db936a2e-f758-4259-9d63-9941edab738e · outbound

This paper cites Enhancing important fluctuations: Rare events and metadynamics from a conceptual viewpoint.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Enhancing important fluctuations: Rare events and metadynamics from a conceptual viewpoint

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.141346Z

Source-reported events for the cited work

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Observation c01c57f2-7e51-4b3a-bb0a-0df753fbfef7 · outbound

This paper cites Variational approach to enhanced sampling and free energy calculations.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Variational approach to enhanced sampling and free energy calculations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.121369Z

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Observation 361399ae-9ef3-40bc-b545-e3892a819cfc · outbound

This paper cites Replica-exchange molecular dynamics method for protein folding.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Replica-exchange molecular dynamics method for protein folding

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.105244Z

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Observation aa8b0fe7-db52-4ec0-a991-6bffa57ef47a · outbound

This paper cites From thermodynamics to kinetics: Enhanced sampling of rare events.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space From thermodynamics to kinetics: Enhanced sampling of rare events

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.089431Z

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Observation 9d3986ba-534c-4acb-bbb6-a56a0efc54e9 · outbound

This paper cites Auto-Encoding Variational Bayes.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Auto-Encoding Variational Bayes

Reference 6

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Source-reported events for the cited work

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Observation 0e2689df-32d2-4f4c-a48a-3b3546f540fa · outbound

This paper cites Coupling molecular dynamics and deep learning to mine protein conformational space.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Coupling molecular dynamics and deep learning to mine protein conformational space

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.074955Z

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Observation 9677d558-664b-4bf2-9eac-aba9ff611e69 · outbound

This paper cites Explore protein conformational space with variational autoencoder.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Explore protein conformational space with variational autoencoder

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.058675Z

Source-reported events for the cited work

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Observation bbaabfc4-47a6-4a53-90ee-8bd1495f5bc0 · outbound

This paper cites Generative adversarial nets.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Generative adversarial nets

Reference 9

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Source-reported events for the cited work

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Observation 39788da6-ec41-402a-ac8e-f3ddf29658cd · outbound

This paper cites Variational Approaches for Auto-Encoding Generative Adversarial Networks.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Variational Approaches for Auto-Encoding Generative Adversarial Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:23:04.137274Z

Source-reported events for the cited work

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Observation 346c2df3-b9b7-44e2-9c9c-0961f19dadd0 · outbound

This paper cites Interpreting latent spaces of generative models for medical images using unsupervised methods.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Interpreting latent spaces of generative models for medical images using unsupervised methods

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:06.031567Z

Source-reported events for the cited work

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Observation 6e5e70e9-6d2b-449d-8f6f-eea7b76773b5 · outbound

This paper cites Generative deep learning for macromolecular structure and dynamics.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Generative deep learning for macromolecular structure and dynamics

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:05.872595Z

Source-reported events for the cited work

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Observation 3ba5b5a6-020d-44c2-be76-030c2598014c · outbound

This paper cites Targeted adversarial learning optimized sampling.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Targeted adversarial learning optimized sampling

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:05.544249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c57f61a7-1a69-4dee-842d-4b1cd2277007 · outbound

This paper cites Medgan: optimized generative adversarial network with graph convolutional networks for novel molecule design.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Medgan: optimized generative adversarial network with graph convolutional networks for novel molecule design

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:05.376735Z

Source-reported events for the cited work

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Observation 656ff25e-bf7e-4468-a301-387e91704277 · outbound

This paper cites MolGAN: An implicit generative model for small molecular graphs.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space MolGAN: An implicit generative model for small molecular graphs

Reference 15

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Source-reported events for the cited work

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Observation bed09906-ad28-4942-82db-add106cbd129 · outbound

This paper cites Ramanet: Computational de novo helical protein backbone design using a long short-term memory generative adversarial neural network.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Ramanet: Computational de novo helical protein backbone design using a long short-term memory generative adversarial neural network

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:05.157237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bf7efc69-a4ed-4826-9bf7-4bf5c13f3179 · outbound

This paper cites Protein loop modeling using deep generative adversarial network.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Protein loop modeling using deep generative adversarial network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:04.972971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a13071ac-80c8-4db9-838b-c6905082fb39 · outbound

This paper cites Generative modeling for protein structures.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Generative modeling for protein structures

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:04.808041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a7a7550-3efd-46bc-adf8-b06d1e40c27b · outbound

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

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Fully differentiable full-atom protein backbone generation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:04.658972Z

Source-reported events for the cited work

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Observation aed1e5ae-af5f-43de-af94-00d6f7a53b1e · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:03.353446Z

Source-reported events for the cited work

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Observation 91cef351-1e71-4d3c-a19c-333266260052 · outbound

This paper cites Improved training of wasserstein gans.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Improved training of wasserstein gans

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:03.429728Z

Source-reported events for the cited work

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Observation 95ae5a6a-8c67-40fc-930b-addbf9e863c2 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:03.522093Z

Source-reported events for the cited work

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Observation 003dd5f8-dac6-42bd-9aee-9942aa6a2b45 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Image-to-image translation with conditional adversarial networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:03.613759Z

Source-reported events for the cited work

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Observation fcc11388-44a8-47d8-8301-f5ad9715fd00 · outbound

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MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:23:04.425289Z

Source-reported events for the cited work

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Observation 7632b556-d53b-446a-84cd-77ab4b629d72 · outbound

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MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:23:04.340955Z

Source-reported events for the cited work

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Observation add0fcf3-b872-4cfc-a348-b23a9c7f9498 · outbound

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

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:03.910831Z

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Pith citing papers

No inbound Pith citation observations are available.