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Paper Citation Record · LEDGER

Geometric deep learning assists protein engineering. Opportunities and Challenges

As of 16 August 2026, this Paper Citation Record lists 100 of 185 outbound references and 0 inbound Pith citation observations for arXiv:2506.16091.

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

pith.paper-citation-record.v1
2506.16091 v1

Coverage vector

measured 100 of 185 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:27.653483Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

100 of 185 outbound references displayed

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External citation measurements

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Outbound references

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This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 1

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This paper cites J., Bambrick, J., et al.

Geometric deep learning assists protein engineering. Opportunities and Challenges J., Bambrick, J., et al

Reference 2

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This paper cites and Das, R.

Geometric deep learning assists protein engineering. Opportunities and Challenges and Das, R

Reference 3

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Reference 4

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 5

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This paper cites Geometric Deep Learning on Molecular Representations.

Geometric deep learning assists protein engineering. Opportunities and Challenges Geometric Deep Learning on Molecular Representations

Reference 6

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Reference 7

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 8

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 9

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This paper cites R., Wang, J., Cong, Q., Kinch, L.

Geometric deep learning assists protein engineering. Opportunities and Challenges R., Wang, J., Cong, Q., Kinch, L

Reference 10

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This paper cites Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch.

Geometric deep learning assists protein engineering. Opportunities and Challenges Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch

Reference 11

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This paper cites Efficient GNN Training Through Structure-Aware Randomized Mini-Batching.

Geometric deep learning assists protein engineering. Opportunities and Challenges Efficient GNN Training Through Structure-Aware Randomized Mini-Batching

Reference 12

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This paper cites S., Elvati, P., Kozarekar, S., VanEpps, J.

Geometric deep learning assists protein engineering. Opportunities and Challenges S., Elvati, P., Kozarekar, S., VanEpps, J

Reference 13

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This paper cites A., Olivares-Gil, A., Rodr \' guez, J.

Geometric deep learning assists protein engineering. Opportunities and Challenges A., Olivares-Gil, A., Rodr \' guez, J

Reference 14

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Geometric deep learning assists protein engineering. Opportunities and Challenges A., Olivares-Gil, A., Rodríguez, J

Reference 15

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Geometric deep learning assists protein engineering. Opportunities and Challenges Relational inductive biases, deep learning, and graph networks

Reference 16

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Geometric deep learning assists protein engineering. Opportunities and Challenges and Wang, B

Reference 17

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Geometric deep learning assists protein engineering. Opportunities and Challenges M., Lima, A

Reference 18

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 19

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Geometric deep learning assists protein engineering. Opportunities and Challenges C., Esvelt, K

Reference 20

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Geometric deep learning assists protein engineering. Opportunities and Challenges M., Bruna, J., Cohen, T., and Veličković, P

Reference 21

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

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Geometric deep learning assists protein engineering. Opportunities and Challenges R., He, X., and Fushimi, R

Reference 26

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Geometric deep learning assists protein engineering. Opportunities and Challenges and Bailey-Kellogg, C

Reference 28

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Geometric deep learning assists protein engineering. Opportunities and Challenges J., Milles, L

Reference 30

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Geometric deep learning assists protein engineering. Opportunities and Challenges Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation

Reference 31

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Geometric deep learning assists protein engineering. Opportunities and Challenges O., Dirks, R

Reference 33

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Geometric deep learning assists protein engineering. Opportunities and Challenges and Malliaros, F

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Geometric deep learning assists protein engineering. Opportunities and Challenges P., Joshi, C

Reference 35

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Geometric deep learning assists protein engineering. Opportunities and Challenges L., and Birol, I

Reference 36

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Geometric deep learning assists protein engineering. Opportunities and Challenges M., and Correia, B

Reference 38

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Observation 04d1f59f-4f27-4034-a408-c972ac26dccd · outbound

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 41

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Observation 134abf7d-781f-4fab-a22f-c5020ddf3ad3 · outbound

This paper cites D., Kosciolek, T., Leman, J.

Geometric deep learning assists protein engineering. Opportunities and Challenges D., Kosciolek, T., Leman, J

Reference 42

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Observation db645dd4-1752-4615-9596-5a51f0c57753 · outbound

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Reference 43

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Observation 0c8f4877-5ae9-4586-9c12-d8fe1be106e5 · outbound

This paper cites G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A.

Geometric deep learning assists protein engineering. Opportunities and Challenges G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A

Reference 44

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Observation 5a193a79-e596-4558-b492-cda7668b92ca · outbound

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Reference 45

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Observation 914fb957-d78b-443c-a955-b986b0697e9d · outbound

This paper cites an unresolved cited work.

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Reference 46

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Observation 2aacb24e-ff7b-4fd9-ae7c-4321afccd6ac · outbound

This paper cites G., Gharabli, H., Acevedo-Rocha, C.

Geometric deep learning assists protein engineering. Opportunities and Challenges G., Gharabli, H., Acevedo-Rocha, C

Reference 47

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Observation bbd2ea7d-24f4-42da-8c16-2a36b5b18dc1 · outbound

This paper cites an unresolved cited work.

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Reference 48

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Observation aad1b4b2-df75-49ab-885f-c7b56c534289 · outbound

This paper cites an unresolved cited work.

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Reference 49

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Observation 79649a30-7ace-42c2-9c41-597ce228c986 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 50

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Observation bf680060-0983-4b3f-8091-304b6c326530 · outbound

This paper cites Strategies for Pre-training Graph Neural Networks.

Geometric deep learning assists protein engineering. Opportunities and Challenges Strategies for Pre-training Graph Neural Networks

Reference 51

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Observation ec2cdf22-5d2b-4f50-801d-ff205afcabd2 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 52

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Observation 0ce02a9b-719e-4638-bbb5-2775abae4be6 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 53

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Observation b7c96adb-00b4-43e1-80f9-a7d53661af97 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 54

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Observation 01df0453-4ea2-4bc2-8bbc-0d3c53980a65 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 55

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Observation f91077f1-c519-47ea-9edd-44e9c749ede8 · outbound

This paper cites B., Baranov, M., Costello, Z., Barber, K.

Geometric deep learning assists protein engineering. Opportunities and Challenges B., Baranov, M., Costello, Z., Barber, K

Reference 56

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Observation d9d49236-923c-4345-bb9f-479941f215e9 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 57

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Observation c6a2bca8-0562-460a-a115-eb16cfd39dff · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 58

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Observation 33fcd80b-1871-40b8-84ba-66d0def1ed67 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 59

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Observation 796e172b-344e-41ba-84f8-8fe7dddf7851 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 60

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Observation 2cc5560b-3f55-4829-aa07-d895d77fcfc1 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 61

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Observation 79e5221e-d2cb-4cbf-a2fd-d60c278aa7b5 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 62

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Observation 7d84d41f-ef77-46c3-822f-c31bee3dfc60 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 63

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Observation dd506a11-a708-492a-b0cb-3aecb01a0985 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 64

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Observation ccdeb8d6-a028-4938-8030-18d89ef66620 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 65

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Observation 6564bc2f-84ec-492d-9007-f1d6d0a31ada · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 66

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Observation aa62a1bd-539d-40f3-93c2-153fa37a1d0f · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 67

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Observation f8a10380-9099-4756-b333-934f9dd8d662 · outbound

This paper cites A Survey on Explainability of Graph Neural Networks.

Geometric deep learning assists protein engineering. Opportunities and Challenges A Survey on Explainability of Graph Neural Networks

Reference 68

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Observation ad01c066-e981-4029-8c53-5abf0632483d · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 69

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Observation 53d03078-3ed0-41bf-bdb7-ca5449d64062 · outbound

This paper cites Variational Graph Auto-Encoders.

Geometric deep learning assists protein engineering. Opportunities and Challenges Variational Graph Auto-Encoders

Reference 70

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Observation 43ccf49a-d79d-4fa5-8da2-a573ec8b4373 · outbound

This paper cites E., and Kolinski, A.

Geometric deep learning assists protein engineering. Opportunities and Challenges E., and Kolinski, A

Reference 71

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Observation 10ebd110-bb89-47bf-9201-d781e5bf8ed4 · outbound

This paper cites F., Abriata, L.

Geometric deep learning assists protein engineering. Opportunities and Challenges F., Abriata, L

Reference 72

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no resolver link, observed 2026-08-06T23:48:25.371962Z

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Observation 24bc2f83-8f42-4117-b723-70f0ae0de3fa · outbound

This paper cites F., Meireles, F.

Geometric deep learning assists protein engineering. Opportunities and Challenges F., Meireles, F

Reference 73

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Observation 6fea7ed9-d90c-4222-b069-02a8aafc4e38 · outbound

This paper cites R., Morey-Burrows, F.

Geometric deep learning assists protein engineering. Opportunities and Challenges R., Morey-Burrows, F

Reference 74

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Observation b43535b7-0daf-4775-93dd-e64fa47745a3 · outbound

This paper cites A Model-Centric Review of Deep Learning for Protein Design.

Geometric deep learning assists protein engineering. Opportunities and Challenges A Model-Centric Review of Deep Learning for Protein Design

Reference 75

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Observation 2fb68a42-b5dd-4b11-b497-2a80d9661a4e · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 76

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Source-reported events for the cited work

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Observation ca58eb51-6fd4-4a53-b15b-4c3677ccaa9f · outbound

This paper cites PepGB: Facilitating peptide drug discovery via graph neural networks.

Geometric deep learning assists protein engineering. Opportunities and Challenges PepGB: Facilitating peptide drug discovery via graph neural networks

Reference 77

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verified exact
local_arxiv, observed 2026-08-06T23:48:35.458885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 416fca97-0582-4064-a804-73e7956c4e4c · outbound

This paper cites and Ming, D.

Geometric deep learning assists protein engineering. Opportunities and Challenges and Ming, D

Reference 78

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Observation 6d5651a2-160d-4fc5-b5f8-a06297cb176e · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 79

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Observation 029efd23-ecc6-4425-8650-6668928e89d6 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 80

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Observation 5ec46277-64b7-45a3-a65f-2598c63f32c7 · outbound

This paper cites and Liu, Z.-P.

Geometric deep learning assists protein engineering. Opportunities and Challenges and Liu, Z.-P

Reference 81

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Observation 84c093aa-d8b6-4a82-a3a3-34ac15846a40 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 82

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This paper cites From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering.

Geometric deep learning assists protein engineering. Opportunities and Challenges From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

Reference 83

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 84

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This paper cites C., Tresadern, G., and Bonnet, P.

Geometric deep learning assists protein engineering. Opportunities and Challenges C., Tresadern, G., and Bonnet, P

Reference 85

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This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 86

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This paper cites and AlQuraishi, M.

Geometric deep learning assists protein engineering. Opportunities and Challenges and AlQuraishi, M

Reference 87

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 88

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 89

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 90

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Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 91

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This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 92

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This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 93

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Source-reported events for the cited work

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Observation 04ad610c-b512-4223-8062-c73f1e19b943 · outbound

This paper cites Aligning Protein Conformation Ensemble Generation with Physical Feedback.

Geometric deep learning assists protein engineering. Opportunities and Challenges Aligning Protein Conformation Ensemble Generation with Physical Feedback

Reference 94

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Geometric deep learning assists protein engineering. Opportunities and Challenges A., Tolomei, G., De Rijke, M., and Silvestri, F

Reference 95

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Observation cb992070-9089-4926-a400-e4d7d2ddb695 · outbound

This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 96

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This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 97

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This paper cites an unresolved cited work.

Geometric deep learning assists protein engineering. Opportunities and Challenges Unresolved cited work

Reference 98

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Source-reported events for the cited work

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Observation 679c667c-d663-48ad-a9c0-a0b134322287 · outbound

This paper cites and Tarakanova, A.

Geometric deep learning assists protein engineering. Opportunities and Challenges and Tarakanova, A

Reference 99

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Observation ee82276f-a427-4a71-8d4a-9c3650ae8bb0 · outbound

This paper cites and Bayzid, M.

Geometric deep learning assists protein engineering. Opportunities and Challenges and Bayzid, M

Reference 100

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Pith citing papers

No inbound Pith citation observations are available.