Antibody sequence generation model that samples paired human VH/VL chains, covering inpainting, inverse folding, and CDR grafting in one network.
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Therapeutic antibody engineering is fragmented into separate computational tasks, each with its own specialist model: generating new paired sequences, filling in a masked region, designing sequence onto a known backbone, and grafting non-human complementarity-determining regions into a human framework. Running four tools with four sets of assumptions is a real drag on discovery pipelines. IgCraft, from the Sormanni lab at the University of Cambridge, collapses all four into one network that samples paired human heavy and light variable domains under whatever conditioning information is on hand.
What makes this possible is the choice of generative framework. IgCraft is built on a discrete-variable Bayesian Flow Network, which denoises a continuous field of per-token logits rather than the tokens themselves. Like a diffusion model, and unlike an autoregressive language model such as p-IgGen, it imposes no generation order and decouples the number of sampling steps from sequence length. That order-agnosticism is what lets a model trained only to produce full-length sequences perform arbitrary-region inpainting at inference time, without a task-specific head. The work was released as a preprint in March 2025 and presented at the GEM workshop at ICLR 2025, with code and the final checkpoint under an MIT license. A companion Zenodo deposit adds the intermediate post-pretraining, pre-fine-tuning weights along with the full 13.6 GB training corpus and the test set, all under CC BY 4.0.
IgCraft contains roughly 300 million trainable parameters. The backbone uses gated self-attention, rotary positional embeddings, pre-layer normalization, and SwiGLU transitions, with adaptive layer normalization in the cross-chain interaction blocks; the structure encoder adopts the geometric attention design from ESM3. Sampling solves the BFN's logit-space SDE with a second-order solver in 20 steps, and conditional sampling uses particle filtering with 32 particles. Training proceeds in three stages: per-chain pretraining on 118 million unpaired VH and 135 million unpaired VL sequences from the Observed Antibody Space, fine-tuning on 1.5 million paired sequences, then structure fine-tuning on roughly 30,000 predicted and 2,800 experimental paired structures with only the structure encoder updated. On a 2,000-sequence holdout, unconditional samples match native repertoire novelty and diversity statistics. Inpainting recovers 91.6% of H-CDR1 and 41.1% of H-CDR3 residues, and 96.7% across all framework regions. On 98 held-out structures, inverse-folded sequences reach 45.0% H-CDR3 recovery — below AntiFold at 58.1% — but IgCraft is the only method whose designs improve both AbNatiV humanness and CamSol solubility over the native sequences on both chains. Grafting mouse CDRs from 27 paired structures yields an OASis humanness of 77.9 against 74.6 for HuDiff and 47.3 for the parental mouse antibodies, with H-CDR3 loop RMSD of 2.04 Å under AlphaFold 3 refolding and 10 of 20 bound complexes correctly docked.
The immediate use is antibody humanization: taking a high-affinity mouse or other non-human binder and generating a human framework that preserves the CDR conformations responsible for binding, with the option of feeding CDR backbone geometry to the model when a structure or prediction exists. Beyond that, the same checkpoint supports library design from unconditional sampling, affinity maturation and developability repair through region-wise inpainting, and sequence design onto a designed or predicted antibody backbone. Because humanness and solubility of the outputs are competitive with dedicated tools, it fits as a single step in a workflow that previously chained several.
IgCraft is the first paired-antibody generative model to combine unconditional and conditional sampling with optional structural conditioning in one trained network, and it makes a concrete case that Bayesian Flow Networks are a practical alternative to both autoregressive and discrete diffusion generators for biological sequences. Its strongest result is in CDR grafting, where it exceeds a diffusion-based humanization model on both humanness metrics despite not being fine-tuned to optimize either. Task-specific models still lead on individual benchmarks: AbLang2, the successor to AbLang, edges it on CDR inpainting recovery, and AntiFold on inverse-folding recovery. All evaluation is computational, relying on predicted humanness, solubility, and structure scores rather than binding measurements, and the paper appeared as a workshop contribution rather than a full peer-reviewed publication.
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