Paper Citation Record · LEDGER

Energy-based generative models for monoclonal antibodies

As of 13 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2411.13390.

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

pith.paper-citation-record.v1
2411.13390 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:35:15.434186Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T21:02:13.242220Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-23T21:03:26.342153Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation f41639af-de58-4c49-90d9-407162a8c4dd · outbound

This paper cites (2017) Biophysical properties of the clinical- stage antibody landscape.

Energy-based generative models for monoclonal antibodies (2017) Biophysical properties of the clinical- stage antibody landscape

Reference 1

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Observation a100d18a-b4a9-409e-a049-8aaa4c3ec91d · outbound

This paper cites (2018) Yeast surface display plat- form for rapid discovery of conformationally selective nanobodies.

Energy-based generative models for monoclonal antibodies (2018) Yeast surface display plat- form for rapid discovery of conformationally selective nanobodies

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.605442Z

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Observation b5ddb6d4-188d-45b0-83c8-98e3b4a63573 · outbound

This paper cites Toxins 10:236.

Energy-based generative models for monoclonal antibodies Toxins 10:236

Reference 3

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Observation 703a85c2-0886-490b-885b-4ef001b5b795 · outbound

This paper cites (2022) Improving antibody affinity through in vitro mutagenesis in complementarity determining re- gions.

Energy-based generative models for monoclonal antibodies (2022) Improving antibody affinity through in vitro mutagenesis in complementarity determining re- gions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.567602Z

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Observation 91428382-4184-48db-982c-f3fe4918dbca · outbound

This paper cites Antibody Engineering: Methods and Proto- cols, Second Edition pp 411–442.

Energy-based generative models for monoclonal antibodies Antibody Engineering: Methods and Proto- cols, Second Edition pp 411–442

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.549471Z

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Observation 061700fe-1a32-4f9c-aaa2-57d8b3c41718 · outbound

This paper cites an unresolved cited work.

Energy-based generative models for monoclonal antibodies Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:35:16.533470Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b7f3b01d-0033-465e-92a3-7a4113c05511 · outbound

This paper cites (2021) Optimization of therapeutic an- tibodies by predicting antigen specificity from antibody sequence via deep learning.

Energy-based generative models for monoclonal antibodies (2021) Optimization of therapeutic an- tibodies by predicting antigen specificity from antibody sequence via deep learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.515337Z

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Observation 78d23047-6e64-43a3-a91a-2938eeab8984 · outbound

This paper cites Nature methods 18:389–396.

Energy-based generative models for monoclonal antibodies Nature methods 18:389–396

Reference 8

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Observation 76e054d4-3d9f-43cd-ae5a-6b1ba40524f5 · outbound

This paper cites (2022) Biological sequence design with gflownets (PMLR), pp 9786–9801.

Energy-based generative models for monoclonal antibodies (2022) Biological sequence design with gflownets (PMLR), pp 9786–9801

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.475825Z

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Observation 0ab392d8-622c-4a04-bd22-911b09a3cf14 · outbound

This paper cites (2024) Atomically accurate de novo design of single-domain antibodies.

Energy-based generative models for monoclonal antibodies (2024) Atomically accurate de novo design of single-domain antibodies

Reference 10

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Observation 7c7910c6-6a8d-40ff-84a4-74d8a53fcdb4 · outbound

This paper cites BioRxiv pp 2021– 12.

Energy-based generative models for monoclonal antibodies BioRxiv pp 2021– 12

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.436251Z

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Observation e74c444e-7f73-409a-9229-d1ef04361710 · outbound

This paper cites Protein Science 31:141–146.

Energy-based generative models for monoclonal antibodies Protein Science 31:141–146

Reference 12

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Observation a2e38f82-fc57-4a3e-907e-10e4a3e91f33 · outbound

This paper cites pp 1352–1361.

Energy-based generative models for monoclonal antibodies pp 1352–1361

Reference 13

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Observation 98d0f6f4-b185-429d-b17d-cd9b707058f3 · outbound

This paper cites (2023) Gflownet foundations (JML- RORG), Vol.

Energy-based generative models for monoclonal antibodies (2023) Gflownet foundations (JML- RORG), Vol

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.368890Z

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Observation 03c40b33-74c0-4aad-bad5-17f23553ff2c · outbound

This paper cites (2022) A dataset comprised of binding interactions for 104,972 antibodies against a sars-cov-2 peptide.

Energy-based generative models for monoclonal antibodies (2022) A dataset comprised of binding interactions for 104,972 antibodies against a sars-cov-2 peptide

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.348686Z

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Observation dacfeb9b-e481-4947-99aa-32092dbb3a2d · outbound

This paper cites Advances in neu- ral information processing systems 31.

Energy-based generative models for monoclonal antibodies Advances in neu- ral information processing systems 31

Reference 16

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Observation dbbd972b-b09e-4028-baee-c65accbe72be · outbound

This paper cites RESP-REP.

Energy-based generative models for monoclonal antibodies RESP-REP

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.636690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d9970a31-238e-4ec9-b49b-393018d7618f · outbound

This paper cites (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model.

Energy-based generative models for monoclonal antibodies (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.289755Z

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Observation d82c194f-977d-443c-b155-c34edebfff42 · outbound

This paper cites Deciphering antibody affinity maturation with language models and weakly supervised learning.

Energy-based generative models for monoclonal antibodies Deciphering antibody affinity maturation with language models and weakly supervised learning

Reference 19

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Observation cc39605d-6aeb-436c-8bc8-1065085ed26c · outbound

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Energy-based generative models for monoclonal antibodies Unresolved cited work

Reference 20

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Observation 16a9452d-680b-4b5c-b4b9-6fcea8f6500c · outbound

This paper cites lysozyme and insulin.

Energy-based generative models for monoclonal antibodies lysozyme and insulin

Reference 21

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Observation da0d480a-f85a-4ae8-ab6a-89073f9b620f · outbound

This paper cites Frontiers in immunology 13:958584.

Energy-based generative models for monoclonal antibodies Frontiers in immunology 13:958584

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.233602Z

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Observation a2532fab-9c5e-45fc-8ee8-4bd1951ab211 · outbound

This paper cites (2017) Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

Energy-based generative models for monoclonal antibodies (2017) Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning

Reference 23

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Observation ada45c5f-a55c-45c8-acd3-7dc29e7bd826 · outbound

This paper cites Journal of molecular biology 427:478– 490.

Energy-based generative models for monoclonal antibodies Journal of molecular biology 427:478– 490

Reference 24

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Observation 6f4aefe8-4084-4a38-a70f-f4ea166671b6 · outbound

This paper cites Scientific reports 7:8200.

Energy-based generative models for monoclonal antibodies Scientific reports 7:8200

Reference 25

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Observation bb85a144-47c2-4300-9c2e-5ba4ef0be7e4 · outbound

This paper cites (2014) Sabdab: the structural antibody database.

Energy-based generative models for monoclonal antibodies (2014) Sabdab: the structural antibody database

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.215718Z

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Observation ae849306-96db-47a4-9475-f187b50b8850 · outbound

This paper cites (2023) Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.

Energy-based generative models for monoclonal antibodies (2023) Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:16.137948Z

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Observation df285f81-650f-4470-905f-41af59923ed0 · outbound

This paper cites Protein Engineering, Design and Selection 4:155–161.

Energy-based generative models for monoclonal antibodies Protein Engineering, Design and Selection 4:155–161

Reference 28

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Observation a0e85dd9-f8b7-4f0d-85ea-1683907ff461 · outbound

This paper cites AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation.

Energy-based generative models for monoclonal antibodies AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation

Reference 29

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Source-reported events for the cited work

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Observation 00a0a2d4-66ec-4902-b71f-e2bcd9369be6 · outbound

This paper cites (2021) Binding affinity landscapes constrain the evolution of broadly neutralizing anti- influenza antibodies.

Energy-based generative models for monoclonal antibodies (2021) Binding affinity landscapes constrain the evolution of broadly neutralizing anti- influenza antibodies

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.922528Z

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Observation 818a3cee-e4c2-4dbf-aab7-4f61e0c41d46 · outbound

This paper cites PLoS computational biology 12:e1004771.

Energy-based generative models for monoclonal antibodies PLoS computational biology 12:e1004771

Reference 31

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Observation 5e4ee352-b0da-452b-a761-4a18eb66e32c · outbound

This paper cites Cell systems 8:86–93.

Energy-based generative models for monoclonal antibodies Cell systems 8:86–93

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.944390Z

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Observation 703548b1-cc2b-4bc1-8c83-38ae1765baad · outbound

This paper cites an unresolved cited work.

Energy-based generative models for monoclonal antibodies Unresolved cited work

Reference 33

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Observation 0874306b-d10f-4ed3-9288-8cba771a8274 · outbound

This paper cites epistemic.

Energy-based generative models for monoclonal antibodies epistemic

Reference 34

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Observation cf177cc7-3065-42c1-8c44-d22f1fe683fa · outbound

This paper cites (2022) Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction.

Energy-based generative models for monoclonal antibodies (2022) Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

Reference 35

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Observation 4e89bdad-d29e-494e-8e3d-ee954d18abca · outbound

This paper cites (year?) Antibody Design with Constrained Bayesian Optimization.

Energy-based generative models for monoclonal antibodies (year?) Antibody Design with Constrained Bayesian Optimization

Reference 36

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80b67821-257a-4700-b104-262c355ac448 · outbound

This paper cites Active learning for affinity prediction of antibodies.

Energy-based generative models for monoclonal antibodies Active learning for affinity prediction of antibodies

Reference 37

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Observation 78ffa371-5ce6-495e-a910-f6114cadf091 · outbound

This paper cites Advances in Neural Infor- mation Processing Systems 34:12849–12863.

Energy-based generative models for monoclonal antibodies Advances in Neural Infor- mation Processing Systems 34:12849–12863

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.825680Z

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Observation e341f779-d0db-477b-aa69-c1175519e7cc · outbound

This paper cites 34, pp 6683–6694.

Energy-based generative models for monoclonal antibodies 34, pp 6683–6694

Reference 39

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80a41f64-ed39-4024-ac77-b5623f6c2751 · outbound

This paper cites an unresolved cited work.

Energy-based generative models for monoclonal antibodies Unresolved cited work

Reference 40

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 81528a11-6412-4ef7-befa-9937c3ece138 · outbound

This paper cites (2024) Information-Directed Pessimism for Offline Reinforcement Learning.

Energy-based generative models for monoclonal antibodies (2024) Information-Directed Pessimism for Offline Reinforcement Learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.750204Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f485aba8-101c-4192-993a-1200135d282f · outbound

This paper cites Bioinformatics 28:3150–3152.

Energy-based generative models for monoclonal antibodies Bioinformatics 28:3150–3152

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.732129Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9390557d-81c5-4b8e-8556-e847cc9c8767 · outbound

This paper cites The Journal of Physical Chemistry B 114:6614–6624.

Energy-based generative models for monoclonal antibodies The Journal of Physical Chemistry B 114:6614–6624

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.709240Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 716ecc55-daf5-423a-b637-20952cb5f8ba · outbound

This paper cites an unresolved cited work.

Energy-based generative models for monoclonal antibodies Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:35:15.687791Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6af4ea66-96fd-449c-a943-a77353ee94b0 · outbound

This paper cites (2022) Language models of protein sequences at the scale of evolution enable accurate structure pre- diction.

Energy-based generative models for monoclonal antibodies (2022) Language models of protein sequences at the scale of evolution enable accurate structure pre- diction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.667234Z

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Observation b57644ef-28e4-4cb7-8983-8c81205c0355 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Energy-based generative models for monoclonal antibodies BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 46

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Source-reported events for the cited work

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Observation 88319933-6a3a-427d-adc8-29e85bd35268 · outbound

This paper cites Neural Machine Translation in Linear Time.

Energy-based generative models for monoclonal antibodies Neural Machine Translation in Linear Time

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T16:35:15.414628Z

Source-reported events for the cited work

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Observation 9d7b4bd3-5375-4370-a49b-330ba2d9e104 · outbound

This paper cites Cell Systems 15:286–294.

Energy-based generative models for monoclonal antibodies Cell Systems 15:286–294

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:35:15.648683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2b7baa6f-b340-432d-84c2-d23e8b975fe0 · outbound

This paper cites (2023) Learning gflownets from partial episodes for improved convergence and stability (PMLR), pp 23467–23483.

Energy-based generative models for monoclonal antibodies (2023) Learning gflownets from partial episodes for improved convergence and stability (PMLR), pp 23467–23483

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:35:15.630421Z

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Pith citing papers

Observation e22430a5-803c-4945-a2d9-87b93bb6568e · inbound

ADIOS: Antibody Development via Opponent Shaping cites this paper.

ADIOS: Antibody Development via Opponent Shaping Energy-based generative models for monoclonal antibodies

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:03:26.344901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.