---
title: An AI-Designed Antibody Matched Months of Lab Work in the First Blinded Test of Its Kind
description: In the first blinded international contest, an AI-designed antibody matched three months of lab work. Field-wide, AI has not yet beaten the bench.
author: Darie Nani (Editor-in-Chief)
updated: 2026-08-27T05:17:30.979Z
canonical: https://www.sovereignmagazine.com/article/aintibody-ai-antibody-design-nature-biotech-212438
image: https://cdn.nanimediahouse.com/aintibody-ai-antibody-design-212438.webp
categories: Artificial Intelligence
content_type: Analysis
region: Global
publication: Sovereign Magazine
schema_type: Article
---

In the first international competition to test AI antibody design against laboratory methods under identical, blinded wet-lab conditions, a computer-designed antibody matched and slightly beat the best result that about three months of bench work produced. The full findings were [published on Aug. 26 in Nature Biotechnology](https://www.nature.com/articles/s41587-026-03238-6), and they mark the clearest head-to-head evidence yet on where AI-designed antibody discovery actually stands.

The contest, called AIntibody, tested 511 antibody designs from 29 organizations. Each team had 14 days per task and could submit up to 10 sequences, all of them then made and measured under the same conditions so no group could grade its own homework. Andrew Bradbury and Frank Erasmus at Specifica, an IQVIA business, designed the competition and laid out its structure in a 2024 Nature Biotechnology paper before the results were run. It covered three tasks: improving an existing antibody's binding strength, ranking candidates by affinity, and designing the binding loops from scratch.

## Aureka's antibody narrowly beat three months of phage-display work

The standout number came from the affinity maturation task, where the goal was to take an existing antibody against the SARS-CoV-2 receptor-binding domain and make it bind more tightly. Aureka Biotechnologies took first, second and fifth place. Its best antibody measured 94.7 picomolar affinity, roughly a 2,000-fold improvement over the antibody it started from.

The strongest antibody produced by conventional laboratory work in that task measured 113 picomolar and had taken about three months of phage-display maturation to reach. The two results are close enough to count as a statistical tie, with the AI design numerically slightly ahead. Aureka also reported six developable antibodies binding below 10 nanomolar that passed the contest's developability screen across five measures, including thermal stability and aggregation. The company built the designs with an in-house foundation model, AuraIDE, trained on protein co-evolution data, and maintains an open-source model called OpenDDE.

Aureka says it has raised close to $200 million to date, including a $100 million Series B in August 2026, on the strength of pharmaceutical partnerships and revenue from live drug-discovery programs.

## Across the whole contest, AI did not beat the bench

The broader picture is more sober. Independent analysis of the full results found that, taken across all three tasks, computational design did not outperform experimental methods on a field-wide basis. Performance varied widely from team to team and task to task, and no single approach dominated. A group that excelled at one task tended not to carry that strength to the others, so success did not transfer.

## AI-designed antibodies are already being tested in people

AI can already design a working antibody, and those molecules have moved past benchmarks into patients. Generate Biomedicines' GB-0895, aimed at severe asthma, is in Phase 3 with two global trials enrolling roughly 1,600 patients. Absci has two AI-designed antibodies, ABS-101 and ABS-201, in Phase 1/2a with early safety data it describes as positive.

No AI-designed antibody has yet been cleared by the U.S. Food and Drug Administration, and every candidate remains in trials.

## The clinical trials still take the same years and money

The distance between winning a design contest and shipping a drug is where the real cost and time still sit. Independent benchmarking using the FLAb2 antibody dataset found that AI models fail to correlate with about 80 percent of the developability properties that determine whether an antibody can become a medicine, and that the models lose roughly 40 percent of their apparent predictive power once you account for how much they simply echo antibodies already in their training data. Immunogenicity and aggregation remain persistent failure points.

The clinical numbers point the same way. For AI-discovered drugs generally, Phase 1 success runs high, around 80 to 90 percent. Phase 2 success drops to about 40 percent, the same rate the industry has posted for years. The pattern suggests AI is compressing the design stage, the fast part, while the slow, expensive work of proving a drug safe and effective in people stays exactly as slow as it always was.

What the contest and the wider evidence leave open is whether that speed reaches the parts of drug development that actually take the years and the money.

## FAQ

**Q: What is the AIntibody competition?**
It is the first international AI antibody design competition to validate entries with independent wet-lab testing. Designed by Andrew Bradbury and Frank Erasmus at Specifica, an IQVIA business, it tested 511 designs from 29 organizations under blinded, uniform conditions across three tasks, with full results published in Nature Biotechnology on Aug. 26, 2026.

**Q: Can AI design antibodies better than a lab can?**
Sometimes, in specific settings. In the contest's affinity maturation task, Aureka's AI-designed antibody measured 94.7 picomolar against a target, matching and slightly beating the 113 picomolar reached by about three months of laboratory work. But across all three tasks, independent analysis found AI did not outperform experimental methods overall, and results varied widely by team and task.

**Q: Has any AI-designed antibody been approved by the FDA?**
No. Several are in human trials, including candidates in Phase 3 and Phase 1/2a, but none has yet been approved.

**Q: Which companies are using AI to design antibodies?**
Aureka Biotechnologies, which won the contest's affinity maturation task, is one. Others with AI-designed antibodies already in clinical trials include Generate Biomedicines, whose asthma candidate GB-0895 is in Phase 3, and Absci, which has two candidates in Phase 1/2a.

**Q: What does an antibody's binding affinity in picomolar mean?**
Affinity measures how tightly an antibody grips its target, and a lower number means a tighter grip. Picomolar binding is very strong. A 94.7 picomolar antibody binds more tightly than a 113 picomolar one, which is why the contest's top result counted as edging ahead of the laboratory-made version.