# Nature Biotechnology | Aureka Wins the Global Blinded AI Antibody Benchmark: AI Design Surpasses the Best Experimental Result

- Link: https://www.thailand-business-news.com/pr-news/nature-biotechnology-aureka-wins-the-global-blinded-ai-antibody-benchmark-ai-design-surpasses-the-best-experimental-result
- Published: 2026-08-27T07:00:00+07:00
- Author: PR Newswire

SHANGHAI and LAGUNA HILLS, Calif., Aug. 27, 2026 /PRNewswire/ — The complete results
of AIntibody, the first international AI antibody design competition, have now been
formally published in _**Nature Biotechnology**_.

[⌊Figure 1 | Screenshot of the Nature Biotechnology paper⌉⌊Figure 1 | Screenshot
of the Nature Biotechnology paper⌉[
Figure 1 | Screenshot of the Nature Biotechnology
paper

Widely regarded as the field’s most rigorous test — every entry validated by independent
wet-lab experiments under identical conditions — this blinded benchmark saw Aureka
Biotechnologies take first place in **Challenge 1 (in-silico affinity maturation)**
with AuraIDE, its in-house antibody design model, entered and cited in the paper
as AuraBind.

The winning antibody designed by AuraIDE reached an affinity of 94.7pM as measured
by KinExA, an approximately **2,000-fold improvement** over the parental antibody.
Two further antibodies submitted by AuraIDE ranked second and fifth, giving the 
model **three of the top five places in Challenge 1**. In addition, AuraIDE designed
a total of **six antibodies with affinities below 10 nM that also met the competition’s
developability criteria**.

**The AIntibody Challenges: A Blinded, Independently Wet-Lab-Validated Benchmark
for AI Antibody Design**

[⌊Figure 2 | Official release of the AIntibody challenges results⌉⌊Figure 2 | Official
release of the AIntibody challenges results⌉[
Figure 2 | Official release of the
AIntibody challenges results

Generative AI is already widely used in antibody discovery and protein design, yet
the field still has no objective way to measure what any given model can actually
do. In protein structure prediction, the blinded-benchmark paradigm established 
by CASP has become the accepted standard for judging a model’s true capability. 
Through prospective blind testing and unified evaluation criteria, it pulls computational
methods out of their own datasets and internal benchmarks and holds them all to 
one common standard — the way AlphaFold2’s breakthrough performance was validated.

AIntibody applies that same approach to antibodies, putting two questions the field
has yet to answer to the test: how good are AI-designed antibodies, really — and
does any model’s design capability hold up under standardized, independent wet-lab
testing?

The first AIntibody challenge targeted the receptor-binding domain (RBD) of the 
SARS-CoV-2 Spike protein. The RBD is among the most thoroughly studied proteins 
in the world, backed by extensive public structural and sequence data, and the organizers
supplied participants with a rich set of experimental screening data on top of that.
That abundance is exactly what makes it a demanding test: with so much of the RBD
already in public training data, a model can only stand out by having genuinely 
learned the rules of antibody binding rather than memorized them.

The competition comprised three tasks. Challenge 1 focused on in-silico affinity
maturation: from organizer-supplied NGS data covering only the first stage of the
parental antibody’s affinity maturation, participants had to design new antibodies
outright — higher in affinity, and developable. Challenge 2 asked participants to
rank existing candidate antibodies by affinity; Challenge 3, to design novel CDR
combinations beyond the screening data.

For Aureka, Challenge 1 was the most important of the three. It was **the only task
built on data from a sequencing pipeline like the ones used in real antibody development,
and the one that maps most directly onto lead optimization as it is actually practiced**.
In its Discussion, _Nature Biotechnology_ further notes that Challenge 1 is, among
this year’s tasks, **the scenario in which the applied value of AI is currently 
clearest**.

The first competition attracted 29 participating organizations and tested 511 AI-
designed or AI-predicted antibodies, with participants spanning academia, non-profit
organizations, AI startups, biotechnology companies, and large pharmaceutical and
technology companies.

Teams had 14 days from the release of each task to submit, and in Challenge 1 no
more than 10 sequences each. All submitted sequences were expressed as full-length
IgG by the organizers under uniform conditions, first measured for affinity by SPR,
with high-affinity candidates then further validated by single-point and standard
KinExA.

Affinity alone was not enough. Each antibody also had to clear five developability
assessments — **HIC, BVP, AC-SINS, Tm and Tagg** — covering hydrophobicity, polyreactivity,
self-interaction, thermal stability and aggregation propensity. Only candidates 
whose composite scores met the competition’s thresholds counted as **developable**.
And throughout, the process stayed blinded: no participant saw any experimental 
result until the competition closed.

In a field awash in technical reports and preprints, a blind test like AIntibody
earns its weight. It does not take a model’s word for its own performance, scored
retrospectively on its own dataset. It puts every team’s model into head-to-head
competition under identical wet-lab conditions, stripping out the distortion of 
experimental variation and selective reporting, so that capability is judged on 
real experimental data and nothing else.

**AuraIDE Wins: Compute in Place of Wet-Lab Cycles, Efficiently Powering Antibody
Affinity Maturation**

[⌊Figure 3 | Schematic of the Challenge 1 affinity maturation task⌉⌊Figure 3 | Schematic
of the Challenge 1 affinity maturation task⌉[
Figure 3 | Schematic of the Challenge
1 affinity maturation task

Challenge 1, which Aureka won, focuses on a critical step in antibody development:
once a hit capable of binding the target has been obtained, how to further improve
affinity without altering the molecular framework and within a defined variable-
region scope, while keeping the molecule developable throughout.

The raw data the competition provided came from NGS sequencing of the parental antibody’s
first affinity maturation stage. Yeast display libraries were constructed and screened
separately for HCDR1, HCDR2, LCDR1, LCDR2 and LCDR3, while HCDR3 and the framework
were held constant. That first-stage sequencing output was the only data the competing
models ever saw.

In a conventional experimental workflow, however, this is only the first step.

From here, researchers would normally recombine the best-performing mutations from
the separate CDR libraries into a combinatorial library and run another round of
wet-lab screening, searching the resulting combinations for the clones with the 
highest affinity.

The data from this round of combinatorial screening, however, was **withheld entirely
from all competing models**.

So the models saw only first-round NGS data, yet had to answer outright a question
that a conventional workflow would settle only through further library construction,
screening and repeated experimentation:

**Which combinations of mutations would actually yield antibodies that are both 
higher in affinity and developable?**

Twenty-five** organizations took on that question, submitting 165 antibody designs**
to Challenge 1. Every one of them was then put through real wet-lab validation under
uniform conditions.

This is what makes Challenge 1 fundamentally different from retrospective prediction
in the usual sense: the models were not reproducing an experimental answer that 
already existed, but proposing directly — with the final experimental results unknown—
the antibody sequences most worth validating in the next round.

**94.7pM: AI Design Surpasses the Best Experimental Result**

[⌊Figure 4-1 | Complete results of the Challenge 1 affinity maturation task⌉⌊Figure
4-1 | Complete results of the Challenge 1 affinity maturation task⌉[
Figure 4-1 
| Complete results of the Challenge 1 affinity maturation task

[⌊Figure 4-2 | Complete results of the Challenge 1 affinity maturation task⌉⌊Figure
4-2 | Complete results of the Challenge 1 affinity maturation task⌉[
Figure 4-2 
| Complete results of the Challenge 1 affinity maturation task

The final results show that the winning antibody designed by AuraIDE, Aureka’s in-
house foundation model, had an affinity (KD) of 94.7pM as measured by KinExA — an
approximately **2,000-fold improvement in affinity** over the parental antibody.

By comparison, the best experimental antibody, obtained through a second round of
combinatorial library construction and wet-lab screening, had a KinExA KD of 113
pM.

In other words, **a sequence AuraIDE designed in under a week outperformed the best
clone from three months of phage maturation experiments**. No other team in the 
competition matched it.

AuraIDE’s designed candidate sequences placed **1st, 2nd and 5th** on the final 
leaderboard — all three among the top five. Across its full submission, Aureka produced**
six antibodies below 10 nM that also met the competition’s developability standard**.
Under blinded conditions and a hard cap on submissions, clustering several candidates
near the top says more about a model’s reliability than any single top hit does.

What that reliability means in practice is this: AI is no longer just triaging which
candidates are worth taking into the lab; it is beginning to do part of the optimization
work that once took repeated rounds of library construction, screening and trial
and error. Aureka also placed third in Challenge 2 and ninth in Challenge 3 — the
only team to finish in the top 10 of all three.

**Starting from Existing Data, Exploring a Broader Sequence Space**

The winning sequence was something new — not a recombination of the high-frequency
mutations already present in the experimental data.

Calculated by Levenshtein distance over the concatenated full-length CDR sequences,
the winning antibody differs from the original parental antibody at **19 amino acid
positions**; even compared with the most similar sequence in the experimental dataset,
it still differs by at least **12 amino acids**. By contrast, the other top-ranked
designs were on the whole closer to existing experimental sequences.

AuraIDE, then, is not mining the existing NGS data for its most frequent mutation
combinations and stitching them together. Instead it extracts from the first round
of experimental data the signal that guides affinity optimization, then explores
sequence space the screening data never reached, generating high-affinity antibodies
that hold up under independent wet-lab validation.

From first-round NGS data to a 94.7pM winning antibody; from a single top hit to
multiple high-affinity, developable candidate sequences — AIntibody’s prospective,
blinded wet-lab benchmark has provided a direct, independent validation of what 
AuraIDE can do in antibody optimization.

For Aureka, though, the win matters for more than first place on a benchmark.

**As compute begins to replace part of a wet-lab process that has depended on repeated
library construction, screening and trial and error, the value of AI in drug discovery
is also beginning to shift from ****"****predicting more accurately****" ****to ****"****
developing more quickly.****"**

[⌊Figure 5 | Aureka’s workflow for the AIntibody challenges⌉⌊Figure 5 | Aureka’s
workflow for the AIntibody challenges⌉[
Figure 5 | Aureka’s workflow for the AIntibody
challenges

Paper: [https://www.nature.com/articles/s41587-026-03238-6](https://www.nature.com/articles/s41587-026-03238-6)

DOI: [https://doi.org/10.1038/s41587-026-03238-6](https://doi.org/10.1038/s41587-026-03238-6)

**About Aureka Biotechnologies**

Aureka Biotechnologies is an AI-native TechBio company dedicated to building a new
generation of biological foundation models and closed-loop, AI-native infrastructure
to reengineer the entire drug discovery process. The company has raised nearly US
$200 million to date and has established strategic collaborations with several leading
global pharmaceutical companies to jointly advance the development of differentiated
antibody therapeutics.

AuraIDE, the company’s in-house foundation model, is trained on proprietary protein
co-evolution data and has established a leading position in protein folding and 
de novo design. Its open-source version, OpenDDE, has passed independent third-party
evaluation and ranks among the world’s leading open-source models; AuraIDE has now
also won the global AI antibody design competition published in _Nature Biotechnology_.
Combining a proprietary single-cell functional screening platform with project-specific
post-training techniques, the company has produced high-value, differentiated antibodies
at scale across a number of programs that are difficult to address with conventional
methods, such as GPCRs and dual-target monoclonal antibodies. Through end-to-end,
agentic R&D infrastructure, it accelerates the translation of innovative concepts
into candidate molecules, providing sustained support for the scaled advancement
of both its internal pipeline and external collaboration projects.

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