Bibliographic record
Abstract
Recent years have witnessed striking advances in miniprotein design, yet de novo antibody discovery remains challenging, marked by low binding rates and the need for extensive, labor-intensive experimental screening of millions of candidates. This technical report introduces GeoFlow-V3, a unified atomic generative model for structure prediction and protein design. GeoFlow-V3 delivers improved accuracy on antibody-antigen complex structure prediction relative to our previous version, and its performance is further enhanced when experimental constraints or prior knowledge are provided, enabling precise control over both folding and design. The model also demonstrates reliable ability to discriminate binders from non-binders based on its confidence scores. Leveraging this capability, we build a GeoFlow-V3 in silico pipeline to design no more than 50 nanobodies per therapeutically relevant target de novo, completing a single round of wet-lab characterization in under three weeks. GeoFlow-V3 identifies at least one binder for 8 tested epitopes and achieves an average hit rate of 15.5%, representing a two-orders-of-magnitude improvement over prior computational pipelines. These results position GeoFlow-V3 as an appealing platform for rapid, AI-driven therapeutic antibody discovery, significantly reducing experimental screening demands and offering a powerful avenue to tackle previously undruggable targets. A demo of GeoFlow-V3 can be accessed via prot.design for non-commercial use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".