Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:35:15.434186Z
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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-23T21:02:13.242220Z
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
- verified exact1
- verified fuzzy38
- unresolved9
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- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.620914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.588452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.496413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.453582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.419916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.398167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.327130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:35:15.258898Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc39605d-6aeb-436c-8bc8-1065085ed26c · outbound
This paper cites an unresolved cited work.
Energy-based generative models for monoclonal antibodies Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T16:35:16.309141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.249641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.267749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.190874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.159745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:15.961171Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:35:15.297886Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:15.898848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T16:35:16.670132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 0874306b-d10f-4ed3-9288-8cba771a8274 · outbound
This paper cites epistemic.
Energy-based generative models for monoclonal antibodies epistemic
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:16.654024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:15.867183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:15.847943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T16:35:15.557022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:35:15.801373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T16:35:15.771166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:35:15.406668Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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 2026-08-12T06:34:41.77262+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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 2026-08-12T06:34:41.77262+00:00.