Computational Antibody Papers
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2025
TitleKey points
2025-12-12
CDR Conformation Aware Antibody Sequence Design with ConformAb
- generative methods
- structure prediction
- ConformAb is a guided discrete-diffusion method for antibody lead optimization that preserves the seed binder’s CDR backbone conformation while introducing sequence diversity.
- Structural preservation is enforced by steering the diffusion process to match the seed’s canonical CDR class probabilities, ensuring generated sequences retain the same canonical backbone geometry.
- Canonical classes are assigned by folding SabDab and pOAS sequences with ABB2 and labeling them using the Kelow et al. dihedral-based canonical clustering scheme; ConformAb learns to predict these classes from sequence.
- During generation, a KL-based guidance signal constrains mutations so that each CDR remains in the seed’s canonical class, enabling safe exploration of sequence space around the functional binder.
- Although ConformAb does not model affinity directly, its structure-preserving diversification enables zero-shot affinity maturation: some variants emerge with improved binding despite using no antigen structure, no repertoire data, and no affinity labels.
- The method was experimentally validated, generated sequences were expressed, tested by SPR on EGFR, IL-6, and a third target, achieving 15–60% binding rates and, for two targets, producing binders with 3–5x higher affinity than the seed.
- Crystal structures of top EGFR and IL-6 binders confirmed that, despite substantial and non-conservative mutations, the CDR backbone conformations were preserved, validating the model’s structural guidance in wet-lab experiments.
2025-11-26
JAM-2: Fully computational design of drug-like antibodies with high success rates
- generative methods
- protein design
- JAM-2 is a novel method for de novo design of biologics that are then experimentally validated and show strong developability (expression, hydrophobicity, polyspecificity, monomericity). More than 57% of all designs pass all core developability criteria straight from the computer jam-2.
- JAM-2 is a generative model, but the details are not revealed.
- The most promising candidates (thousands per target in epitope-tiling mode, ~45 per format in target-level mode) are tested for binding by yeast display (epitope mode) or BLI (target mode) ; the entire discovery timeline is ≈ 1 month, with 2–3 days of fully computational design upfront, matching exactly what the paper reports jam-2.
- Hit rates: Across 16 completely unseen targets: 39% average hit-rate for VHH-Fcs 18% average hit-rate for mAbs 100% of targets produced at least one binder These are all double-digit success rates from only 45 designs per format jam-2.
- VHHs have higher hit rates but generally weaker affinities.
- A panel of several hundred antibodies was assessed for: hydrophobicity, self-association (polyspecificity), expression titer, monomericity, thermostability. More than half (57%) met all pass criteria simultaneously, and 80%+ passed individual criteria such as expression or hydrophobicity. These molecules were not optimized, it was the first pass from the model.
2025-11-26
Drug-like antibody design against challenging targets with atomic precision
- protein design
- generative methods
- developability
- Update on earlier Chai-2 results adding developability and structural validations.
- Previously generated scFv hits were reformatted into full-length IgGs; ~93% retained binding.
- Developability was assessed using NanoDSF (Tm), HIC-HPLC, BVP ELISA, and AC-SINS, with Jain-style green-flag thresholds.
- Most reformatted IgGs passed ≥3 of 4 developability flags, indicating good biophysical properties without further optimization.
- Newly designed antibodies were generated for the GPCR benchmarks, showing successful in silico design against challenging targets.
2025-11-17
BLOSUM Is All You Learn — Generative Antibody Models Reflect Evolutionary Priors
- generative methods
- binding prediction
- Benchmarking of 'old-school' substition matrices to see whether they can guide affinity maturation of antibodies.
- they took several datasets wherte the parental antibody is known and the mutants affinities are known as well
- they compared a diffusion based model, inverse folding and the substitution matrices.
- they note that blosum performs surprisingly well.
- 'training' the substitution matrix on the mutants with respect to the wild type yields better results than making it off sabdab or oas.
2025-11-17
ODesign: A World model for biomolecular interaction design.
- generative methods
- protein design
- structure prediction
- New biomolecular generative algorithm for protein/molecular design
- It extends AlphaFold3 architecture into a generative “world model” that designs interactions across proteins, nucleic acids, and small molecules using a shared token space and conditional diffusion.
- High-throughput in-silico design: It achieves up to 100- to 1000-fold higher computational throughput than diffusion or hallucination baselines (RFDiffusion, BoltzDesign, etc.) across 11 computational benchmark tasks.
- Only computational benchmarks presented.
2025-10-28
peleke-1: A Suite of Protein Language Models Fine-Tuned for Targeted Antibody Sequence Generation
- language models
- generative methods
- Novel LLM suite for designing antibodies.
- Peleke-1 models were fine-tuned on 9,500 antibody–antigen complexes from SAbDab, each annotated with interacting residues identified from crystal structures.
- Structure was incorporated by annotating epitope residues explicitly in antigen sequences, allowing the LLMs to learn binding context without direct 3D input.
- Generated antibodies were assessed for humanness, structural validity, stability (FoldX), and binding affinity (HADDOCK3) across seven benchmark antigens.
- No wet-lab testing was performed.
- Novel de novo antibody design method.
- Trained on SAbDab with a time split—6,448 heavy+light complexes + 1,907 single-chain (nanobodies), clustered at 95% ID into 2,436 clusters; val/test are 101 and 60 complexes, plus 27 nanobodies.
- A two-stage diffusion (structure→seq+structure) followed by consistency distillation, epitope-aware conditioning, frozen ESM-PPI features, and mixed task sampling (CDR-H3 / heavy CDRs / all CDRs / no seq).
- Antigen structure (can warm-start from AlphaFold3) + VH/VL framework sequences; you pick which CDRs (and lengths) to design; model outputs CDR sequences and the full complex.
- Runs without an epitope but docking drops (DockQ ~0.246 → 0.069, SR 0.433 → 0.050); AF3 initialization lifts success to 0.627 (≈+0.19 vs baseline).
2025-09-30
Efficient generation of epitope-targeted de novo antibodies with Germinal
- generative methods
- nanobodies
- protein design
- Novel open nanobody design method with experimental validation.
- On the surface it might appear like a lot of methods stitched together. The magic sauce appears to be in the joint, gradient-based co-optimization: AF-Multimer and IgLM gradients are merged through a 3-phase schedule (logits → softmax → semi-greedy), with CDR-masking/framework bias and custom losses that force CDR-mediated, loop-like interfaces; then AbMPNN edits only non-contact CDR residues, and designs are filtered independently with AF3 + PyRosetta.
- All this is actually not a ‘trained’ model but rather a filtering pipeline that WAS NOT trained (using previous methods, gradients, weights etc.) Just validated experimentally.
- Experimental benchmark was ran on four targets: PD-L1, IL-3, IL-20, and BHRF1.
- Authors measured how different their designs weren’t just ‘regurgitations’ of known abs. CDR identities were computed against SAbDab and OAS (via MMseqs); many designs show <50% CDR identity to any public sequence.
2025-09-30
mBER: Controllable de novo antibody design with million-scale experimental screening
- binding prediction
- generative methods
- protein design
- experimental techniques
- Novel de novo antibody design method with massive experimental testing.
- The computational method involves integration, not retraining, of existing tools. It combines AlphaFold-Multimer, protein language models (ESM2/AbLang2), and NanoBodyBuilder2 with templating/sequence priors to design/filter antibody-format binders.
- They perform massive testing. >1.1 million VHH binders designed across 436 targets (145 tested); ~330k experimentally screened.
- Hit rates look low per binder (~0.5–1%) but that’s ~50× above random libraries, and still yields thousands of validated binders.
- Target-level success is 45%, for how many targets we got binders; some epitopes reached 30–38% hit rates after filtering.
- The big caveat is the specificity of epitopes- it really makes a difference, with some epitopes producing nought.
- Novel model for loop retrieval using embedded structural representation.
- It is a multimodal tokenizer at the antibody loop (CDR) level that fuses sequence with backbone dihedral-angle features and learns a latent space with a dihedral-distance contrastive loss—unlike residue-tokenizers and canonical clusters. It produces both continuous and quantized loop tokens that can plug into PLMs (IGLOOLM / IGLOOALM).
- Trained by self-supervised on ~807k loops from experimental (SAbDab/STCRDab) and Ibex-predicted structures, with four objectives: masked dihedral reconstruction, masked AA prediction, contrastive learning over dihedral distance (with DTW alignment), and codebook learning; followed by two-phase training and specific H100 settings.
- It was benchmarked on a set of computational goals: for H3 loops IGLOO beats the best prior tokenizer by +5.9% (dihedral-distance criterion). (2) Cluster recovery: high purity vs. canonical clusters across CDRs. (3) Downstream PLM task: IGLOOLM improves binding-affinity prediction on 8/10 AbBiBench targets, rivaling larger models. (4) Controllable sampling: IGLOOALM generates diverse sequences with more structure consistency than inverse-folding baselines.