pith. sign in

arxiv: 2605.21610 · v1 · pith:TTHMPXHRnew · submitted 2026-05-20 · 💻 cs.LG

AgForce Enables Antigen-conditioned Generative Antibody Design

Mansoor Ahmed , Murray Patterson This is my paper

Pith reviewed 2026-05-22 09:17 UTC · model grok-4.3

classification 💻 cs.LG
keywords antibody designantigen conditioninggenerative modelinggraph neural networksmixture density networkssequence recoveryprotein engineering
0
0 comments X

The pith

AgForce conditions antibody design on antigen structure by blocking framework shortcuts and replacing cross-entropy loss.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing antibody design methods largely ignore the antigen input and generate nearly identical CDRs regardless of the target antigen. The paper traces this to three failure modes: reliance on antibody framework context, collapse of predictions to only three to five amino acids per position, and convergence to the positional marginal distribution under standard cross-entropy training. AgForce counters these with an encoder that applies framework dropout, gated bottlenecks, and hyperbolic cross attention, plus a decoder that uses a mixture density network head with annealed multiple choice learning and an antigen cycle consistency head. On the CHIMERA-Bench dataset the resulting model improves amino acid recovery by 8 percent over the strongest baseline while raising all interface metrics and nearly doubling effective vocabulary size. A sympathetic reader would care because reliable antigen-conditioned generation could produce more targeted antibody candidates for specific disease targets.

Core claim

AgForce is a graph-neural-network encoder-decoder that prevents antibody framework shortcuts through framework dropout, gated bottlenecks, and hyperbolic cross attention, while a mixture density network sequence head with Potts-like pairwise coupling and annealed multiple choice learning replaces cross-entropy, and an antigen cycle consistency head routes gradients through the decoder to force predicted distributions to encode antigen identity, yielding the best binding quality and sequence recovery on CHIMERA-Bench together with an 8 percent gain in amino acid recovery and nearly double the effective vocabulary of prior GNN methods.

What carries the argument

AgForce encoder-decoder architecture that uses framework dropout, gated bottlenecks, and hyperbolic cross attention to block antibody shortcuts and a mixture density network head with annealed multiple choice learning plus antigen cycle consistency to enforce antigen-specific sequence distributions.

If this is right

  • Generative models can now output distinct CDR sequences for distinct antigens even when antibody frameworks are similar.
  • Training objectives other than per-position cross-entropy can avoid convergence to positional marginal distributions.
  • Antigen cycle consistency supplies a gradient signal that forces sequence predictions to carry antigen identity.
  • GNN-based antibody design methods can reach effective vocabularies nearly twice as large while preserving or improving binding metrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same shortcut-prevention and cycle-consistency techniques may help other conditional generation tasks where context can override the intended conditioning input.
  • Cycle consistency could be tested in broader protein-protein interface design problems beyond antibodies.
  • Whether the reported metric gains translate to higher experimental success rates in binding assays remains an open next step.

Load-bearing premise

The three failure modes are the dominant causes of antigen blindness and the listed components directly eliminate them rather than trading one set of biases for another.

What would settle it

Generate CDRs for the same antibody framework paired with two dissimilar antigen structures; if the outputs remain nearly identical the antigen-conditioning claim is false.

Figures

Figures reproduced from arXiv: 2605.21610 by Mansoor Ahmed, Murray Patterson.

Figure 1
Figure 1. Figure 1: (a) Vocabulary collapse: Heatmap of the ground truth amino acid distribution of CDR H3, the predictions by dyMEAN, and our proposed model, AGFORCE. (b) AAR vs. effective vocabulary. unchanged [Li et al., 2025], and including the antigen sequence does not improve design quality [Kim et al., 2024]. These findings point to a systematic failure of existing conditioning mechanisms, but no prior work has identif… view at source ↗
Figure 2
Figure 2. Figure 2: shows the overall pipeline. The encoder corrupts heavy-chain framework embeddings via dropout, encodes the complex with a VirtualNode￾EGNN, and computes CDR-to-epitope attention on the Lorentz hyperboloid. The decoder is an MDN￾Potts head trained via annealed multiple choice learning. We detail each component below; train￾ing and inference algorithms are in Appendix A.3 [PITH_FULL_IMAGE:figures/full_fig_p… view at source ↗
Figure 3
Figure 3. Figure 3: AGFORCE architecture. Encoder: Framework dropout corrupts heavy-chain framework embeddings before the VirtualNode-EGNN encodes the complex. Hyperbolic cross-attention com￾bines CDR and epitope embeddings with projected ESM-2 features. Decoder: The MDN-Potts head predicts amino acid distributions through K = 4 mixture components with pairwise coupling, trained via aMCL. Antigen classification loss routes gr… view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the vocabulary collapse failure mode of the baselines vs. A [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
read the original abstract

Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input. We identify three failure modes that explain this behavior. Antigen blindness arises because models derive predictions from antibody framework context rather than antigen information, producing nearly identical CDRs regardless of the target. Vocabulary collapse reduces predicted amino acids to three to five per position, far below the ground truth distribution in native sequences. Moreover, any model trained with standard per-position cross-entropy converges to the positional marginal distribution, making it provably unable to produce antigen-specific sequence predictions. We propose a novel encoder-decoder architecture called AgForce, that uses a graph neural network (GNN) as the encoder and specialized decoders for sequence-structure co-design. Specifically, we apply framework dropout, gated bottlenecks, and hyperbolic cross attention that prevent the antibody shortcut path. In the decoder, a Mixture Density Network (MDN) sequence head with Potts-like pairwise coupling and annealed Multiple Choice Learning (aMCL) replaces the cross-entropy objective with a multi-component distribution whose optimal solution differs from the positional marginal. An antigen cycle consistency head routes gradients through the sequence decoder, forcing predicted distributions to encode antigen identity. AgForce achieves the best binding quality and sequence recovery simultaneously on the CHIMERA-Bench dataset, improving amino acid recovery by 8% over the strongest sequence baseline while surpassing the baselines across all interface metrics, and nearly doubling the effective vocabulary of GNN methods. The source code is available at: https://github.com/mansoor181/ag-force.git

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces AgForce, a GNN-based encoder-decoder architecture for antigen-conditioned CDR sequence-structure co-design. It identifies three failure modes in baselines (antigen blindness via framework shortcuts, vocabulary collapse, and convergence to positional marginals under per-position cross-entropy) and proposes targeted fixes including framework dropout, gated bottlenecks, hyperbolic cross attention, an MDN head with Potts-like coupling and annealed MCL, plus an antigen cycle-consistency loss. On CHIMERA-Bench the method reports the best simultaneous binding quality and sequence recovery, with an 8% recovery gain over the strongest baseline and nearly doubled effective vocabulary.

Significance. If the performance gains and the attribution to the proposed components hold under rigorous verification, the work would advance conditional generative modeling for antibody design by demonstrating concrete architectural and objective-function changes that make antigen information load-bearing. Public code release is a positive factor for reproducibility.

major comments (2)
  1. [Abstract] Abstract: The claim that 'any model trained with standard per-position cross-entropy converges to the positional marginal distribution, making it provably unable to produce antigen-specific sequence predictions' is not supported by a derivation. Per-position CE is minimized by the conditional p(aa_i | full input), which can incorporate antigen features provided the encoder routes them; the manuscript must show in the methods or theory section why this bias is independent of architecture and loss choice rather than an artifact of GNN message passing or optimization.
  2. [Results] Results / Experiments: The reported 8% amino-acid recovery improvement and superiority across interface metrics on CHIMERA-Bench are given without error bars, number of independent runs, or statistical significance tests. In addition, the manuscript lacks ablation tables that isolate the contribution of each component (framework dropout, hyperbolic cross attention, MDN+aMCL, cycle consistency) to the mitigation of the three named failure modes.
minor comments (2)
  1. [Methods] The description of the annealed Multiple Choice Learning (aMCL) schedule and the Potts-like pairwise terms would be clearer with an explicit algorithmic box or pseudocode.
  2. Acronyms such as MDN, aMCL, and CHIMERA-Bench should be expanded at first use in the main text for accessibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major comment point by point below, providing our honest assessment and indicating the revisions we plan to make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'any model trained with standard per-position cross-entropy converges to the positional marginal distribution, making it provably unable to produce antigen-specific sequence predictions' is not supported by a derivation. Per-position CE is minimized by the conditional p(aa_i | full input), which can incorporate antigen features provided the encoder routes them; the manuscript must show in the methods or theory section why this bias is independent of architecture and loss choice rather than an artifact of GNN message passing or optimization.

    Authors: We appreciate the referee's observation that the abstract claim requires stronger theoretical support. The per-position cross-entropy loss is indeed minimized by the conditional distribution given the full input, which could in principle use antigen information. However, our work focuses on the practical failure mode observed across multiple GNN baselines, where antigen features are not effectively utilized due to shortcut learning from framework residues. To address this rigorously, we will add a dedicated paragraph in the Methods section analyzing the loss and optimization dynamics, including a derivation showing how independent per-position predictions combined with limited cross-attention capacity lead to marginal-like solutions when antigen routing is weak. We will also revise the abstract to replace 'provably unable' with 'empirically converges to' while retaining the motivation for our proposed objectives. This revision clarifies the scope without overstating generality. revision: partial

  2. Referee: [Results] Results / Experiments: The reported 8% amino-acid recovery improvement and superiority across interface metrics on CHIMERA-Bench are given without error bars, number of independent runs, or statistical significance tests. In addition, the manuscript lacks ablation tables that isolate the contribution of each component (framework dropout, hyperbolic cross attention, MDN+aMCL, cycle consistency) to the mitigation of the three named failure modes.

    Authors: We agree that the experimental section would be strengthened by statistical rigor and targeted ablations. In the revised manuscript we will report mean performance and standard deviation across five independent runs using different random seeds. We will add p-values from paired statistical tests (e.g., Wilcoxon signed-rank) to support the reported 8% recovery gain and interface metric improvements. We will also include a new ablation table that isolates each component's contribution by measuring its effect on the three failure modes: antigen dependence (prediction change when antigen is masked), effective vocabulary size, and binding metrics. This will directly link framework dropout, gated bottlenecks, hyperbolic cross attention, the MDN head with Potts coupling and annealed MCL, and cycle consistency to the observed gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper's core argument proceeds from empirical identification of three failure modes in existing GNN baselines on CHIMERA-Bench, followed by architectural proposals (framework dropout, gated bottlenecks, hyperbolic cross attention, MDN head with aMCL, antigen cycle consistency) whose necessity is justified by benchmark gains rather than by any equation or parameter that reduces to its own inputs by construction. No self-citations, uniqueness theorems, or ansatzes imported from prior author work appear in the provided text; the public code link supplies an external reproducibility check. The claim that per-position cross-entropy is provably limited to the positional marginal is asserted as a mathematical observation but is not used as a load-bearing derivation that collapses back onto fitted values or self-referential definitions within the paper itself. The work therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond standard deep-learning components; the central claims rest on empirical benchmark results rather than additional postulated entities.

pith-pipeline@v0.9.0 · 5815 in / 1160 out tokens · 39954 ms · 2026-05-22T09:17:07.310563+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    We propose a novel encoder-decoder architecture called AgForce, that uses a graph neural network (GNN) as the encoder and specialized decoders for sequence-structure co-design... MDN sequence head with Potts-like pairwise coupling and annealed Multiple Choice Learning (aMCL)

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

300 extracted references · 300 canonical work pages · 5 internal anchors

  1. [1]

    Mansoor Ahmed and Nadeem Taj and Imdad Ullah Khan and Hemanth Venkateswara and Murray Patterson , booktitle=

    work page

  2. [2]

    biorxiv , title =

    Chen, Xingyao and Dougherty, Thomas and Hong, Chan and Schibler, Rachel and Zhao, Yi and Sadeghi, Reza and Matasci, Naim and Wu, Yi-Chieh and Kerman, Ian , year =. biorxiv , title =

    work page

  3. [3]

    Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods

    Ye, Chao and Hu, Wenxing and Gaeta, Bruno. Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods. JMIR Bioinform Biotech. 2022. doi:10.2196/29404

    work page doi:10.2196/29404 2022

  4. [4]

    and Friedensohn, Simon and Weber, C

    Mason, Derek M. and Friedensohn, Simon and Weber, C. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning , journal=. 2021 , month=. doi:10.1038/s41551-021-00699-9 , url=

    work page doi:10.1038/s41551-021-00699-9 2021

  5. [5]

    MAbs , volume=

    Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods , author=. MAbs , volume=. 2021 , organization=

    work page 2021

  6. [6]

    Antibody stability: A key to performance - Analysis, influences and improvement , journal =

    Hui Ma and Ciarán Ó’Fágáin and Richard O’Kennedy , keywords =. Antibody stability: A key to performance - Analysis, influences and improvement , journal =. 2020 , issn =. doi:https://doi.org/10.1016/j.biochi.2020.08.019 , url =

    work page doi:10.1016/j.biochi.2020.08.019 2020

  7. [7]

    doi:https://doi.org/10.1111/cbdd.13388 , url =

    Muhammed, Muhammed Tilahun and Aki-Yalcin, Esin , title =. doi:https://doi.org/10.1111/cbdd.13388 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1111/cbdd.13388 , year =

    work page doi:10.1111/cbdd.13388

  8. [8]

    Journal of Biomedical Science , year=

    Lu, Ruei-Min and Hwang, Yu-Chyi and Liu, I-Ju and Lee, Chi-Chiu and Tsai, Han-Zen and Li, Hsin-Jung and Wu, Han-Chung , title=. Journal of Biomedical Science , year=. doi:10.1186/s12929-019-0592-z , url=

    work page doi:10.1186/s12929-019-0592-z

  9. [9]

    Grange, R. D. and Thompson, J. P. and Lambert, D. G. , title = ". BJA: British Journal of Anaesthesia , volume =. 2014 , month =. doi:10.1093/bja/aet293 , url =

    work page doi:10.1093/bja/aet293 2014

  10. [10]

    Frontiers in Immunology , VOLUME=

    Huang, Yan and Zhang, Ziding and Zhou, Yuan , TITLE=. Frontiers in Immunology , VOLUME=. 2022 , URL=. doi:10.3389/fimmu.2022.1053617 , ISSN=

    work page doi:10.3389/fimmu.2022.1053617 2022

  11. [11]

    PLOS ONE , publisher =

    Assisted Design of Antibody and Protein Therapeutics (ADAPT) , year =. PLOS ONE , publisher =. doi:10.1371/journal.pone.0181490 , author =

    work page doi:10.1371/journal.pone.0181490

  12. [12]

    and Ascher, David B

    Pires, Douglas E.V. and Ascher, David B. , title = ". Nucleic Acids Research , volume =. 2016 , month =. doi:10.1093/nar/gkw458 , url =

    work page doi:10.1093/nar/gkw458 2016

  13. [13]

    Khamis and Walid Gomaa and Walaa F

    Mohamed A. Khamis and Walid Gomaa and Walaa F. Ahmed , keywords =. Machine learning in computational docking , journal =. 2015 , issn =. doi:https://doi.org/10.1016/j.artmed.2015.02.002 , url =

    work page doi:10.1016/j.artmed.2015.02.002 2015

  14. [14]

    and Andersen, Jan Terje and Greiff, Victor , title=

    Akbar, Rahmad and Bashour, Habib and Rawat, Puneet and Robert, Philippe A. and Andersen, Jan Terje and Greiff, Victor , title=. mAbs , year=. doi:10.1080/19420862.2021.2008790 , url=

    work page doi:10.1080/19420862.2021.2008790 2021

  15. [15]

    and Lehmann, Paul V

    Ras-Carmona, Alvaro and Lehmann, Alexander A. and Lehmann, Paul V. and Reche, Pedro A. , title=. Scientific Reports , year=. doi:10.1038/s41598-022-18021-1 , url=

    work page doi:10.1038/s41598-022-18021-1

  16. [16]

    BMC Genomics , year=

    Ren, Jing and Song, Jiangning and Ellis, John and Li, Jinyan , title=. BMC Genomics , year=. doi:10.1186/s12864-017-3493-0 , url=

    work page doi:10.1186/s12864-017-3493-0

  17. [17]

    BepFAMN : A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network

    La Marca, Anthony F and Lopes, Robson da S and Lotufo, Anna Diva P and Bartholomeu, Daniella C and Minussi, Carlos R. BepFAMN : A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network. Sensors (Basel)

    work page

  18. [18]

    Bioinformatics , volume =

    Liberis, Edgar and Veličković, Petar and Sormanni, Pietro and Vendruscolo, Michele and Liò, Pietro , title = ". Bioinformatics , volume =. 2018 , month =. doi:10.1093/bioinformatics/bty305 , url =

    work page doi:10.1093/bioinformatics/bty305 2018

  19. [19]

    Bioinformatics , volume=

    Paragraph—antibody paratope prediction using graph neural networks with minimal feature vectors , author=. Bioinformatics , volume=. 2023 , publisher=

    work page 2023

  20. [20]

    2025 , doi =

    Ahmed, Mansoor and Ali, Sarwan and Jan, Avais and Khan, Imdad Ullah and Patterson, Murray , title =. 2025 , doi =

    work page 2025

  21. [21]

    Antibodies , volume=

    In silico methods in antibody design , author=. Antibodies , volume=. 2018 , publisher=

    work page 2018

  22. [22]

    Mabs , volume=

    Artificial intelligence-driven computational methods for antibody design and optimization , author=. Mabs , volume=. 2025 , organization=

    work page 2025

  23. [23]

    arXiv preprint arXiv:2506.04235 , year=

    AbBiBench: A Benchmark for Antibody Binding Affinity Maturation and Design , author=. arXiv preprint arXiv:2506.04235 , year=

    work page arXiv

  24. [24]

    PloS one , volume=

    OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes , author=. PloS one , volume=. 2014 , publisher=

    work page 2014

  25. [25]

    Attentive Cross-Modal Paratope Prediction , journal =

    Deac, Andreea and Veli. Attentive Cross-Modal Paratope Prediction , journal =. 2019 , doi =

    work page 2019

  26. [26]

    Nature Biotechnology , year=

    A trimodal protein language model enables advanced protein searches , author=. Nature Biotechnology , year=. doi:10.1038/s41587-025-02836-0 , url=

    work page doi:10.1038/s41587-025-02836-0

  27. [27]

    Nucleic acids research , year=

    Chailyan, Anna and Tramontano, Anna and Marcatili, Paolo , title=. Nucleic acids research , year=. doi:10.1093/nar/gkr806 , url=

    work page doi:10.1093/nar/gkr806

  28. [28]

    and Johnson, David S

    Lim, Yoong Wearn and Adler, Adam S. and Johnson, David S. , title=. mAbs , year=. doi:10.1080/19420862.2022.2069075 , url=

    work page doi:10.1080/19420862.2022.2069075 2022

  29. [29]

    Briefings in bioinformatics , volume=

    AntiFormer: graph enhanced large language model for binding affinity prediction , author=. Briefings in bioinformatics , volume=. 2024 , publisher=

    work page 2024

  30. [30]

    Bioinformatics , volume=

    CSM-AB: Graph-based antibody--antigen binding affinity prediction and docking scoring function , author=. Bioinformatics , volume=. 2022 , publisher=

    work page 2022

  31. [31]

    Structure , volume=

    ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning , author=. Structure , volume=. 2024 , publisher=

    work page 2024

  32. [32]

    Nature Machine Intelligence , volume=

    A topology-based network tree for the prediction of protein--protein binding affinity changes following mutation , author=. Nature Machine Intelligence , volume=. 2020 , publisher=

    work page 2020

  33. [33]

    Journal of Computational Biology , volume=

    Reads2vec: Efficient embedding of raw high-throughput sequencing reads data , author=. Journal of Computational Biology , volume=. 2023 , publisher=

    work page 2023

  34. [34]

    Algorithms , volume=

    Robust representation and efficient feature selection allows for effective clustering of sars-cov-2 variants , author=. Algorithms , volume=. 2021 , publisher=

    work page 2021

  35. [35]

    Bioinformatics Research and Applications: 17th International Symposium, ISBRA 2021, Shenzhen, China, November 26--28, 2021, Proceedings 17 , pages=

    A k-mer based approach for sars-cov-2 variant identification , author=. Bioinformatics Research and Applications: 17th International Symposium, ISBRA 2021, Shenzhen, China, November 26--28, 2021, Proceedings 17 , pages=. 2021 , organization=

    work page 2021

  36. [36]

    Biology , volume=

    Exploring the Potential of GANs in Biological Sequence Analysis , author=. Biology , volume=. 2023 , publisher=

    work page 2023

  37. [37]

    Bioinformatics , volume=

    DLAB: deep learning methods for structure-based virtual screening of antibodies , author=. Bioinformatics , volume=. 2022 , publisher=

    work page 2022

  38. [38]

    Bioinformatics , volume =

    Myung, Yoochan and Pires, Douglas E V and Ascher, David B , title = ". Bioinformatics , volume =. 2021 , month =. doi:10.1093/bioinformatics/btab762 , url =

    work page doi:10.1093/bioinformatics/btab762 2021

  39. [39]

    Machine learning prediction of Antibody-Antigen binding: dataset, method and testing , journal=

    Ye, Chao and Hu, Wenxing and Gaëta, Bruno , year =. Machine learning prediction of Antibody-Antigen binding: dataset, method and testing , journal=

    work page

  40. [40]

    Bioinformatics , volume =

    Jain, Tushar and Boland, Todd and Lilov, Asparouh and Burnina, Irina and Brown, Michael and Xu, Yingda and Vásquez, Maximiliano , title = ". Bioinformatics , volume =. 2017 , month =. doi:10.1093/bioinformatics/btx519 , url =

    work page doi:10.1093/bioinformatics/btx519 2017

  41. [41]

    Frontiers in Microbiology , VOLUME=

    Kang, Tae Hyun and Seong, Baik Lin , TITLE=. Frontiers in Microbiology , VOLUME=. 2020 , URL=. doi:10.3389/fmicb.2020.01927 , ISSN=

    work page doi:10.3389/fmicb.2020.01927 2020

  42. [42]

    Antibody apparent solubility prediction from sequence by transfer learning , journal =

    Jiangyan Feng and Min Jiang and James Shih and Qing Chai , keywords =. Antibody apparent solubility prediction from sequence by transfer learning , journal =. 2022 , issn =. doi:https://doi.org/10.1016/j.isci.2022.105173 , url =

    work page doi:10.1016/j.isci.2022.105173 2022

  43. [43]

    Alejandro and Charonis, Spyros and Curtis, Robin and Warwicker, Jim , title =

    Hebditch, Max and Carballo-Amador, M. Alejandro and Charonis, Spyros and Curtis, Robin and Warwicker, Jim , title =. 2017 , journal =. doi:10.1093/bioinformatics/btx345 , url =

    work page doi:10.1093/bioinformatics/btx345 2017

  44. [44]

    Computational and artificial intelligence-based methods for antibody development

    Kim, Jisun and McFee, Matthew and Fang, Qiao and Abdin, Osama and Kim, Philip M. Computational and artificial intelligence-based methods for antibody development. Trends Pharmacol Sci

    work page

  45. [45]

    Frontiers in immunology , volume=

    DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations , author=. Frontiers in immunology , volume=. 2024 , publisher=

    work page 2024

  46. [46]

    Antibody Therapeutics , volume =

    Zhang, Weijie and Wang, Hao and Feng, Nan and Li, Yifeng and Gu, Jijie and Wang, Zhuozhi , title = ". Antibody Therapeutics , volume =. 2022 , month =. doi:10.1093/abt/tbac029 , url =

    work page doi:10.1093/abt/tbac029 2022

  47. [47]

    1996 , issn =

    Computational methods for biomolecular docking , journal =. 1996 , issn =. doi:https://doi.org/10.1016/S0959-440X(96)80061-3 , url =

    work page doi:10.1016/s0959-440x(96)80061-3 1996

  48. [48]

    Protein structure prediction

    Deng, Haiyou and Jia, Ya and Zhang, Yang. Protein structure prediction. Int. J. Mod. Phys. B

    work page

  49. [49]

    MAbs , volume=

    Antibodies to watch in 2019 , author=. MAbs , volume=. 2019 , organization=

    work page 2019

  50. [50]

    MAbs , volume=

    Phage display and hybridoma generation of antibodies to human CXCR2 yields antibodies with distinct mechanisms and epitopes , author=. MAbs , volume=. 2014 , organization=

    work page 2014

  51. [51]

    Immunology today , volume=

    Antibodies in diagnostics--from immunoassays to protein chips , author=. Immunology today , volume=. 2000 , publisher=

    work page 2000

  52. [52]

    Nature , volume=

    Highly accurate protein structure prediction with AlphaFold , author=. Nature , volume=. 2021 , publisher=

    work page 2021

  53. [53]

    Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning (February 11, 2023) , year=

    Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning , author=. Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning (February 11, 2023) , year=

    work page 2023

  54. [54]

    and Vendruscolo, Michele

    Sormanni, Pietro and Aprile, Francesco A. and Vendruscolo, Michele. Third generation antibody discovery methods: in silico rational design. Chem. Soc. Rev. 2018. doi:10.1039/C8CS00523K

    work page doi:10.1039/c8cs00523k 2018

  55. [55]

    Nucleic acids research , volume=

    PyIgClassify: a database of antibody CDR structural classifications , author=. Nucleic acids research , volume=. 2015 , publisher=

    work page 2015

  56. [56]

    Current Opinion in Structural Biology , volume=

    Advances in computational structure-based antibody design , author=. Current Opinion in Structural Biology , volume=. 2022 , publisher=

    work page 2022

  57. [57]

    Bioinformatics , volume =

    Dai, Bowen and Bailey-Kellogg, Chris , title = ". Bioinformatics , volume =. 2021 , month =

    work page 2021

  58. [58]

    Scientific reports , volume=

    Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation , author=. Scientific reports , volume=. 2021 , publisher=

    work page 2021

  59. [59]

    bioRxiv , pages=

    Advancing Protein-DNA Binding Site Prediction: Integrating Sequence Models and Machine Learning Classifiers , author=. bioRxiv , pages=. 2023 , publisher=

    work page 2023

  60. [60]

    Current opinion in virology , volume=

    Antibody specific epitope prediction—emergence of a new paradigm , author=. Current opinion in virology , volume=. 2015 , publisher=

    work page 2015

  61. [61]

    2024 , publisher=

    Wang, Chuan and Wang, Jiangyuan and Song, Wenjun and Luo, Guanzheng and Jiang, Taijiao , journal=. 2024 , publisher=

    work page 2024

  62. [62]

    epitope1D: accurate taxonomy-aware

    Silva, Bruna and Ascher, David and Pires, Douglas , journal=. epitope1D: accurate taxonomy-aware. 2023 , publisher=

    work page 2023

  63. [63]

    Bioinformatics , volume=

    Learning context-aware structural representations to predict antigen and antibody binding interfaces , author=. Bioinformatics , volume=. 2020 , publisher=

    work page 2020

  64. [64]

    Liu, Chunan and Denzler, Lilian and Chen, Yihong and Martin, Andrew and Paige, Brooks , journal=

    work page

  65. [65]

    A structure-based

    Lu, Shuai and Li, Yuguang and Ma, Qiang and Nan, Xiaofei and Zhang, Shoutao , journal=. A structure-based. 2022 , publisher=

    work page 2022

  66. [66]

    Scientific Reports , volume=

    Prediction of protein--protein interaction using graph neural networks , author=. Scientific Reports , volume=. 2022 , publisher=

    work page 2022

  67. [67]

    Frontiers in immunology , volume=

    SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning , author=. Frontiers in immunology , volume=. 2022 , publisher=

    work page 2022

  68. [68]

    Nucleic Acids Research , volume=

    SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs , author=. Nucleic Acids Research , volume=. 2023 , publisher=

    work page 2023

  69. [69]

    Briefings in bioinformatics , volume=

    Critical review of conformational B-cell epitope prediction methods , author=. Briefings in bioinformatics , volume=. 2023 , publisher=

    work page 2023

  70. [70]

    Nature protocols , volume=

    The ClusPro AbEMap web server for the prediction of antibody epitopes , author=. Nature protocols , volume=. 2023 , publisher=

    work page 2023

  71. [71]

    Journal of Biological Chemistry , volume=

    Cloning and characterization of deoxymugineic acid synthase genes from graminaceous plants , author=. Journal of Biological Chemistry , volume=. 2006 , publisher=

    work page 2006

  72. [72]

    coli , author=

    Small ubiquitin-like modifier protein 3 enhances the solubilization of human bone morphogenetic protein 2 in E. coli , author=. Applied biochemistry and biotechnology , volume=. 2018 , publisher=

    work page 2018

  73. [73]

    Journal of Experimental Botany , volume=

    Paralogs and mutants show that one DMA synthase functions in iron homeostasis in rice , author=. Journal of Experimental Botany , volume=. 2017 , publisher=

    work page 2017

  74. [74]

    , author=

    Effects of MDL 19205 (piroximone), a new cardiotonic agent, on electrophysiological, mechanical, and intracellular ionic characteristics of sheep cardiac tissues. , author=. Journal of cardiovascular pharmacology , volume=

    work page

  75. [75]

    Nature communications , volume=

    Histone H4 lysine 20 mono-methylation directly facilitates chromatin openness and promotes transcription of housekeeping genes , author=. Nature communications , volume=. 2021 , publisher=

    work page 2021

  76. [76]

    Nature communications , volume=

    Histone H4K20 methylation mediated chromatin compaction threshold ensures genome integrity by limiting DNA replication licensing , author=. Nature communications , volume=. 2018 , publisher=

    work page 2018

  77. [77]

    Plant molecular biology , volume=

    Iron deficiency regulated OsOPT7 is essential for iron homeostasis in rice , author=. Plant molecular biology , volume=. 2015 , publisher=

    work page 2015

  78. [78]

    Nucleic Acids Research , volume=

    Topokaryotyping demonstrates single cell variability and stress dependent variations in nuclear envelope associated domains , author=. Nucleic Acids Research , volume=. 2018 , publisher=

    work page 2018

  79. [79]

    in vivo biotinylation

    PUB-NChIP—“in vivo biotinylation” approach to study chromatin in proximity to a protein of interest , author=. Genome research , volume=. 2013 , publisher=

    work page 2013

  80. [80]

    Journal of proteome research , volume=

    PUB-MS: a mass spectrometry-based method to monitor protein--protein proximity in vivo , author=. Journal of proteome research , volume=. 2011 , publisher=

    work page 2011

Showing first 80 references.