AI Antibody Design in 2025 (blog.booleanbiotech.com)

2025 has been a breakout year for AI-driven antibody design: teams moved beyond mini‑binders into full antibody fragments (Fab, scFv, VHH) and de novo binder pipelines that directly target therapeutic classes. The central technical hurdle remains the same as before—accurately modeling highly variable CDR loops that lack helpful evolutionary signal—so architectures blend structure prediction (AlphaFold‑style models, diffusion samplers) with antibody language models and sequence‑level optimization. Early diffusion work like RFantibody showed feasibility but required screening thousands of candidates; newer systems aim to cut that down dramatically. Chai‑2 (June 2025) claims orders‑of‑magnitude reduction in sampling effort and achieved binding for ~50% of test targets with some sub‑nanomolar hits, while other commercial efforts (Nabla, Diffuse, PXDesign) report strong performance on hard targets like GPCRs or offer accessible sandboxes. The landscape is a mix of open and closed tooling with practical tradeoffs: open frameworks such as Mosaic and FreeBindCraft let researchers compose losses, incorporate AbLang, and optimize CDRs, whereas many high‑performance suites (IgGM, Germinal, Chai‑2) depend on proprietary components (PyRosetta, licensed language models) or remain server‑only. Benchmarks like the AIntibody competition are starting to standardize evaluation, but inconsistent metrics (e.g., binding assays at high concentrations) and suspicious reporting in some papers mean independent validation remains crucial. For practitioners, the takeaway is clear: AI now produces therapeutically credible leads faster, but reproducibility, licensing friction, and CDR folding robustness are the next bottlenecks to solve.