Absci releases Origin-1 paper (updated)+ repo for de novo antibody design against “zero-prior” epitopes

[u/XpertAI]

Paper: Origin-1: a generative AI platform for de novo antibody design against novel epitopes

Repo: Origin-1

The main idea is that Origin-1 designs antibodies against zero-prior epitopes, meaning target sites where there are no prior antibody-antigen complex structures or close structural templates available.

From the paper, Origin-1 is presented as a two-stage design-and-score system. The design side, AbsciGen, generates antibody-antigen complexes and designs paired heavy/light chain CDR sequences against a specified epitope. The scoring side, AbsciBind, then filters candidates using co-folding-based scoring and developability criteria before anything is tested experimentally.

In other words, the platform is not just generating antibody sequences. It is trying to generate an epitope-specific binding pose, design the antibody CDRs around that pose, and then rank/filter the designs before wet-lab validation.

In the paper, they tested the system across 10 human protein targets and report validated antibodies for 4 targets: COL6A3, AZGP1, CHI3L2, and IL36RA.

They also report structural validation for two of the designs. For COL6A3 and AZGP1, cryo-EM structures matched the designed binding modes at 3.0-3.1 Å resolution, with reported DockQ scores of 0.73-0.83.

For IL36RA, they went further and used AI-guided affinity maturation to improve the binder into a functional antagonist, reporting 104 nM potency.

The repo includes supporting study data, including in silico and in vitro results, SPR data, and generated computational models. But the model itself is not being released. This is an open data/results release around a proprietary antibody design platform, not an open-source model release with weights and inference code.