Computational Antibody Papers

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  • 2025-07-18

    IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction

    • structure prediction
    • Introduced a novel foundational protein folding model, IntFold, designed for both general and specialized biomolecular structure prediction.
    • Builds on architectural principles from AlphaFold 3, while adding key innovations like modular adapters, a custom attention kernel, and training-free structure ranking. While it does not outperform AlphaFold 3.
    • While it does not outperform AlphaFold 3, IntFold exceeds all other public reproductions of AF3 across multiple biomolecular tasks.
    • The fine-tuned IntFold+ model achieves 43.2% accuracy on antibody-antigen interfaces, approaching AlphaFold 3’s 47.9%.

  • 2025-07-18

    Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins

    • structure prediction
    • Introduces IBEX, a pan‑immunoglobulin structure predictor for antibodies, nanobodies, and TCRs that explicitly models both bound (holo) and unbound (apo) conformations via a conformation token.
    • Training data comprise ~14 000 high‑quality antibody (SAbDab) and TCR (STCRDab) structures (including 760 matched apo/holo pairs), augmented by distillation from ~60 000 predicted immunoglobulin‑like structures to improve generalization (from OAS, modeled with ESMFOld and Boltz-1).
    • Architecture builds on AlphaFold2’s invariant‑point‑attention and the ABodyBuilder2 framework, adding a residual connection from the initial embedding into every structure module and feeding an apo/holo token at each block.
    • Performance on a private benchmark of 286 novel antibodies shows IBEX achieves mean CDR‑H3 RMSD = 2.28 Å, outperforming Chai‑1 (2.55 Å), Boltz‑1 (2.30 Å), and Boltz‑2 (2.42 Å). Most of its advantage arises from greater robustness to sequences whose CDR‑H3 loops have larger edit distances to any structure in the training set.

  • 2025-06-24

    AbEpiTope-1.0: Improved antibody target prediction byuse of AlphaFold and inverse folding

    • binding prediction
    • structure prediction
    • A novel method that repurposes AlphaFold-2.3 structure predictions and combines them with inverse folding–based machine learning models to assess antibody-antigen binding accuracy and specificity.
    • They generate antibody-antigen complex models using AlphaFold-2.3 and evaluate them using the 'AbAgIoU' metric, which measures the overlap between predicted and true epitope/paratope residues — penalizing both missing and extra contacts.
    • They demonstrate that the learned scores can distinguish true from incorrect antibody-antigen pairings (including swapped antibody scenarios), significantly outperforming random baselines.
    • The method relies only on antibody and antigen sequences as input, using AlphaFold to model structures — making it applicable in real-world settings where experimental structures are unavailable.

  • 2025-06-05

    PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models:

    • structure prediction
    • PSBench is a large benchmark dataset (>1M models) for training and evaluating model accuracy estimation (EMA) methods for protein complex structures, using data from CASP15 & CASP16.
    • Models were generated by AlphaFold2-Multimer and AlphaFold3 under blind prediction conditions and annotated with 10 detailed global, local, and interface quality scores.
    • The dataset enables development of advanced EMA methods (e.g. GATE), which showed top performance in blind CASP16 assessments.

  • 2025-03-31

    AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction

    • epitope prediction
    • docking
    • structure prediction
    • Benchmarking of the structure prediction/docking and co-folding methods for antibody design
    • Authors measure the impact of antibody-antigen model quality on the success rate of epitope prediction and antibody design.
    • For epitope prediction and antibody design they use a proxy measure of DockQ score - they call success when DockQ is better than 0.23, for antibody design they use a stricter threshold of 0.49.
    • Using these measures, AlphaFold3 comes out on top, and it would be successful roughly ~47% times.
    • THey introduce an approach where ProPOSE and ZDOCK decoys are refined using AlphaFold. With this combined protocol they reach success rates of 35% for epitope mapping and 30% for antibody design.

  • 2025-03-11

    Fast and accurate antibody sequence design via structure retrieval

    • databases
    • generative methods
    • structure prediction
    • Inverse folding and thus antibody design via database search.
    • Authors train a vector retrieval database on SAbDab. In this way for a single sequence one can figure out where it falls structurally.
    • They benchmark against state of the art inverse folding tools such as AbMPNN, AntiFold, ProteinMPNN and ESM-IF - their tools comes on top in terms of sequence retrieval.
    • The database search is orders of magnitude faster than the state of the art inverse folding tools.
    • They compare IgSeek versus FoldSeek - their tool gets a higher accuracy in sequence retrieval, for most CDRs, but CDR-H3. Therefore FoldSeek seems like a very good choice alongside IgSeek for such a database-driven inverse folding protocol.

  • 2024-10-24

    Chai-1: Decoding the molecular interactions of life

    • structure prediction
    • Foundational model following in the footsteps of AlphaFold3 attempting prediction of molecular interactions.
    • Model architecture is closely modeled on this of AF3 - however it was not benchmarked against it (nor ESM3) because of use restrictions.
    • It takes 30 days on 128 A100s to train the model. Back of the envelope Google Colab pro+ pricing of 128 (which is NOT distributed) puts it at ca. 120k USD :)
    • Addressing antibodies, they introduce constraints (e.g. known epitope residue) to help the model out. Adding even a single residue makes a big difference with respect to baseline which is tantamount to global docking.
    • It only takes one residue as constraints to improve ab-ag complex prediction. Success rate for ‘local’ mode, with just one residue is about 50% in getting it with DockQ score >.21, 30% >.49 and less than 10% for high quality hits.
    • Success rate for ‘global’ mode, so without constraints is about 35% in getting it with DockQ score >.21, 20% >.49 and less than 5% for high quality hits.
    • So altogether if I want to ‘hit the epitope’, the model has ca. 30% success rate.
    • If I want a high quality ab-ag complex structure, unfortunately it seems that constraints do not help much currently.

  • 2024-10-08

    Accurate antibody loop structure prediction enables zero-shot design of target-specific antibodies

    • generative methods
    • structure prediction
    • Authors describe how using a structure predictor one can re-design the binding site, to maintain binding.
    • They use a proprietary GaluxDesign method, the method achieves 1.4 Å Ca RMSD in predicting CDR-H3 loop structures, leveraging a unique scoring metric (G-pass rate) that assesses both confidence and structural consistency for antibody design.
    • The method outperforms AlphaFold 2.3, ABlooper, and ImmuneBuilder in predicting CDR-H3 loop structures, with significantly lower RMSD values (1.4 Å compared to 2.4-3.7 Å), particularly on a more challenging, time-separated dataset.
    • The binding propensity to HER2 was evaluated using a large mutant library and calculated via the G-pass rate, outperforming AlphaFold's PAE-based scoring. The model showed strong discrimination with an AUROC of 0.758, compared to 0.529 for AlphaFold. The novel loop is scored using their metric (G-pass rate) in complex with Her2.
    • Novel antibody sequences were designed by predicting six CDR loops in antibody-protein complexes, using GaluxDesign models. These designs were experimentally tested, achieving high success rates, including a 13.2% success rate for HER2 antibody designs using yeast display methods.

  • 2024-09-04

    Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling

    • structure prediction
    • Novel CDR-H3 structure prediction method, ComMat based on ensemble sampling.
    • Rather than generating a single structure, the method generates several solutions that are then all informing the next iteration.
    • The method was integrated into the structure module of AlphaFold2.
    • Crucially, with the introduction of the second prediction into the ‘community’, the predictions become better. However these quickly plateau, showing the limits of the approach.
    • The method does not produce better results than ABodyBuilder2 and EquiFold.

  • 2024-08-21

    ABodyBuilder3: Improved and scalable antibody structure predictions

    • structure prediction
    • language models
    • Updated version of the popular ABodyBuilder2 program to model antibodies, that is more efficient.
    • Unlike ABB2 that did not employ language model embeddings, ABB3 does (ProtT5 to be precise).
    • Instead of using several models and aggregating the results for error prediction, they train pLDDT within the model.
    • ABB3 achieves CDR-H3 RMSD in the ballpark of 2.4Å whereas the previous version in the region of 2.5Å.