Computational Antibody Papers

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generative methods
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2025
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  • 2025-07-25

    AntiDIF: Accurate and Diverse Antibody Specific Inverse Folding with Discrete Diffusion

    • generative methods
    • AntiDIF, a diffusion-based inverse folding method specialized for antibodies, built on the RL-DIF framework.
    • It is trained using antibody-specific data (from SAbDab and OAS) to generate diverse and accurate antibody sequences for a given backbone structure.
    • Unlike prior methods like AntiFold, which trade off diversity for recovery, AntiDIF achieves a better trade-off: it produces substantially higher sequence diversity across CDRs while maintaining comparable or higher sequence recovery.
    • Forward folding (via ABodyBuilder2) confirms that AntiDIF's sequences fold into structures that match the native antibody backbones with low RMSD, demonstrating structural plausibility.

  • 2025-07-03

    Antibody Design using Chai-2

    • generative methods
    • binding prediction
    • protein design
    • Introduced a novel model, Chai-2, that shows over 100× improvement in de novo antibody design success rates compared to prior methods.
    • The model is prompted with the structure of the target, epitope residues, and desired antibody format (e.g., scFv or VHH).
    • Benchmarking was performed on 52 antigens that had no known antibodies in the PDB, ensuring evaluation on novel, unbiased targets.
    • Generated antibodies were structurally and sequentially dissimilar to any known antibodies, indicating that Chai-2 designs novel binders, not memorized ones.
    • For VHH (nanobody) formats, the model achieved an experimental hit rate of 20%, validated in a single experimental round.

  • 2025-06-24

    Benchmark for Antibody Binding Affinity Maturation and Design

    • binding prediction
    • language models
    • generative methods
    • databases
    • Benchmark of machine learning models for antibody-antigen binding affinity.
    • A curated dataset of over 150,000 antibody-antigen complexes with associated experimental affinity values is compiled from literature.
    • The benchmark compares a wide range of model types: language models, inverse folding models, graph-based, and diffusion-based generative models.
    • Inverse folding models that are globally structure-aware perform best.
    • General protein models like ESM-IF and ProteinMPNN outperform antibody-specific models such as AntiFold, DiffAb, and dyMEAN.
    • Surprisingly, ESM-3 underperforms relative to ESM-IF, despite incorporating structural signals and improving upon earlier ESM models.

  • 2025-06-24

    REPURPOSING ALPHAFOLD3-LIKE PROTEIN FOLDING MODELS FOR ANTIBODY SEQUENCE AND STRUCTURE CO-DESIGN

    • generative methods
    • protein design
    • Novel method to design antibodies based on boltz-1.
    • They added a sequence head to boltz-1 to perform simultaneous sequence/structure co-design.
    • They employed data from SAbDab to fine-tune boltz-1 on antibody-antigen complexes.
    • They compared to dyMEAN and DiffAB looking at amino acid recovery, RMSD and Rosetta InterfaceAnalyzer energy - their model does better on these computational benchmarks.

  • 2025-06-05

    Adapting ProteinMPNN for antibody design without retraining

    • protein design
    • generative methods
    • Novel method to bias ProteinMPNN for antibody design, without modifying model weights.
    • Logits from protein-general ProteinMPNN and antibody-specific AbLANG are added and softmaxed. Addition of AbLANG is supposed to push the model into the antibody-acceptable space.
    • On in-silico experiments ProteinMPNN+AbLang outperformed ProteinMPNN alone and rivalled antibody-specific AbMPNN.
    • Authors designed 96 variants of Trastuzumab CDR-H3 using ProteinMPNN, AbLang and ProteinMPNN+AbLang each. AbLANG and ProteinMPNN produced 1 and 3 successful variants respecitively (both out of 96) whereas their combination produced 36 successful variants.
    • None of the variants were better variants than WT Trastuzumab.

  • 2025-06-05

    AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks

    • developability
    • generative methods
    • protein design
    • Novel generative modeling framework (AbBFN2) using Bayesian Flow Networks (BFNs) for antibody sequence optimization.
    • Trains on sequences from Observed Antibody Space (OAS) combined with genetic and biophysical annotations, leveraging a denoising approach for both conditional and unconditional sequence generation. Targets include optimizing Therapeutic Antibody Profiler (TAP) annotations.
    • Computationally validated for germline assignment accuracy, species prediction (humanness), and TAP parameter optimization.
    • Combines multiple antibody design objectives into a unified, single-step optimization process, unlike existing software methods which are typically specialized for individual tasks.

  • 2025-03-31

    IgCraft: A versatile sequence generation framework for antibody discovery and engineering

    • generative methods
    • language models
    • Novel generative model for antibodies that allows one to fill in, inpaint inverse fold etc.
    • The model employs Bayesian Flow Networks which is somewhat similar to diffusion.
    • The model is trained on data from OAS - unpaired data as a first pass and paired data as a second pass.
    • Models are benchmarked on a range of computational metrics, chiefly sequence recovery (for infiling/inverse folding).
    • Developability is checked by computational prediction of solubility (CamSol) and humanness (AbNativ)

  • 2025-03-21

    How well do contextual protein encodings learn structure, function, and evolutionary context?

    • generative methods
    • protein design
    • Novel inverse folding algorithm, studying the effect of pretraining on the effectiveness of Antibody design
    • Authors check multiple inverse folding regimens, pretraining on general proteins, ppi interfaces and antibody-antigen interfaces and likewise finetuning on these.
    • They only use the backbone atoms (N,C,Ca), with special provisions for Cb.
    • They mask portion of the sequence and have the model guess its amino acids.
    • The 37% recovery at 100% masking appears slightly lower than the same feat for proteinMPNN.
    • Pretraining on antibodies still holds a signal towards antibody-antigen complexes, showing the power of such pre-training.

  • 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.

  • 2025-03-11

    Generation of antigen-specific paired heavy-light chain antibody sequences using large language models

    • generative methods
    • New antigen-specific language model
    • Authors curated a dataset of antigen-specific antbiody seqeunces and fine tuned generic protein language model (don’t know which one) to it.
    • Dataset appears to be comprised mostly of plabdab and cov-abdab so very biased towards covid.
    • Antibodies are generated by prompting the model with the antigone sequence and generating the antibody on the basis of it.
    • Authors tested the generated antibodyes in the lab, including COVID antigens but also some that were less prevalent in the training set and they found binders.