Computational Antibody Papers
Filter by tags
protein design
Filter by published year
2025
TitleKey points
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.
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.
2025-07-25
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding
- protein design
- Introduces a novel diffusion-based inverse folding method (RL-DIF) that improves the foldable diversity of generated sequences—i.e., it can generate more diverse sequences that still fold into the desired structure.
- The model uses categorical denoising diffusion for sequence generation, followed by reinforcement learning (DDPO) to improve structural consistency with the target fold.
- During reinforcement learning, ESMFold is used to predict the 3D structure of generated sequences, which is then compared (via TM-score) to the structure predicted from the native sequence to ensure they fold similarly.
- Compared to baselines like PiFold and ProteinMPNN, RL-DIF achieves similar sequence recovery and structural consistency but significantly better foldable diversity—a critical advantage in protein design.