Computational Antibody Papers
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2026-01-20
Origin-1: a generative AI platform for de novo antibody design against novel epitopes
- protein design
- De novo platform for epitope-specific antibody design against “zero-prior” targets, i.e. antigen sites with no known antibody–antigen or protein–protein complex structures and limited homology to previously solved interfaces.
- The method combines three tightly integrated components: AbsciDiff, an all-atom diffusion model fine-tuned from Boltz-1 to generate epitope-conditioned antibody–antigen complex structures; IgDesign2, a structure-conditioned paired heavy–light CDR sequence design model; and AbsciBind, a modified AF-Unmasked / AlphaFold-Multimer–based scoring protocol using ipTM-derived interface confidence to rank and filter designs.
- The platform was evaluated on 10 zero-prior protein targets, with fewer than 100 antibody designs per target advanced to experimental testing; specific binders were successfully identified for 4 targets (COL6A3, AZGP1, CHI3L2, IL36RA).
- Experimental validation demonstrated both structural and functional accuracy, including cryo-EM confirmation at near-atomic resolution (DockQ 0.73–0.83) for two targets and AI-guided affinity maturation yielding a functional IL36RA antagonist with ~100 nM potency.
2026-01-09
Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2
- protein design
- All-atom, zero-shot generative model that designs antibody sequence and structure directly in complex with a target from epitope-conditioned prompts.
- One specifies target, epitope, modality and the algorithm produces designs.
- They tested 4–24 designs per target, achieving 50% target-level success, producing VHHs and scFvs with pico- to nanomolar affinities (best ≈ 26 pM).
- Designed antibodies show therapeutic-grade developability (expression, aggregation, hydrophobicity, polyreactivity, stability) without optimization via wetlab validation.
- Human PBMC assays (10 donors) show no detectable immunogenicity for representative de novo nanobodies.
2025-12-12
Predicting Experimental Success in De Novo Binder 2 Design: A Meta-Analysis of 3,766 Experimentally 3 Characterised Binders
- protein design
- databases
- Large-scale benchmarking of structural, energetic, and confidence metrics to distinguish protein binders from non-binders.
- Curated 3,766 experimentally tested de novo binders across 15 targets from independent campaigns.
- Of these, 436 were confirmed binders, the remainder non-binders.
- Each design was re-modelled using AF2 (initial guess + ColabFold), Boltz-1, and AF3.
- From these predictions they computed 200+ structural and confidence descriptors.
- AF3-derived confidence scores (especially ipSAE_min) were the best single discriminators, although per-target precision still ranged widely (0.1–1.0), underscoring strong target dependence.
2025-12-12
AntiBMPNN: Structure-Guided Graph Neural Networks forPrecision Antibody Engineering
- protein design
- Novel inverse folding algorithm for antibodies with experimental validation.
- It uses atom-level graph MPNN, structured transformer, novel scoring and AF3 filtering; unlike ProteinMPNN/AbMPNN/AntiFold, which operate residue-level and lack downstream optimization.
- Only experimental antibody structures (free antibodies + complexes) of antibodies were used for training.
- AntiBMPNN uses a distinct dataset; AbMPNN and AntiFold rely heavily on modeled structures, unlike AntiBMPNN. So it might just be that the moderate gains in residue retrieval are due to a slightly bigger dataset used.
- Unlike most models they actually performed experimental validation: ELISA assays on huJ3 (single-points, CDR1, CDR3) and D6 (CDR2), with multiple variants improving binding over wild type.
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
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.
- Novel protein design framework based on a unified all-atom diffusion model that performs both structure prediction and binder generation.
- It is fully open and free.
- Training setup resembles recent diffusion architectures (e.g., AlphaFold3, Chai), but its distinguishing feature is broad wet-lab validation across diverse target types.
- Experimental scale: generated tens of thousands of nanobody and protein designs for 9 novel targets (no homologous complexes in PDB).
- Results: tested 15 designs per target, obtaining nanomolar binders for 6 of 9 targets (≈66% success rate) — a notably strong experimental outcome.
- 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.