LAGUNA HILLS, Calif. And SHANGHAI, August 10, 2026 — Aureka Biotechnologies announced the close of a US$100 million Series B financing on Aug. 10, 2026. Granite Asia funded the first tranche exclusively, and a prominent strategic investor led a subsequent tranche, with participation from HighLight Capital (HLC) and follow-on investment from existing shareholders including MPCi and NRL Capital. Aureka has now raised nearly US$200 million to date.

The company will direct proceeds primarily toward research and large-scale training of its next generation of biological foundation models, further strengthening performance on core tasks such as de novo molecular design, biological structure modeling and function prediction. Aureka will also upgrade Lab-in-the-Loop, its experiment-centered feedback engine, strengthening the closed-loop between those models and its proprietary single-cell functional screening, high-throughput experimental validation and drug development platforms.

With its closed-loop, AI-native infrastructure already built, Aureka is now strengthening the intelligence core of that system: its foundation models. Aureka combines large-scale pre-training, project-specific post-training, AI agents and experiments that run at scale into AI-for-Science infrastructure for the life sciences. In it, models do not just solve individual drug discovery tasks; they learn the rules of biology, to understand, generate, predict and intervene in complex biological systems.

As foundation models and automated R&D converge, Aureka is shifting from using AI to make drug discovery more efficient to using AI to model living systems, pushing both the technical frontier and the commercial ceiling of AI-driven drug discovery.

Closed-Loop AI-Native Infrastructure Builds a Stronger Intelligence Core

Founded in 2023, Aureka Biotechnologies is an AI-native TechBio company developing a new generation of biological foundation models and closed-loop infrastructure that surrounds them, combining AI models, agents, digital biology and experimental platforms to redesign the drug discovery process end to end.

Biology does not yield to computation alone; it depends on feedback from the physical world. Sustained improvement in large biological models requires more than advances in compute, algorithms and model architecture. It also takes high-quality experimental data that faithfully reflects molecular function, and an experimental system able to continuously test model hypotheses, correcting model bias and feeding results into the next iteration.

Aureka therefore treats Lab-in-the-Loop as core infrastructure for model development, integrating AI agents, high-throughput digital biology, proprietary single-cell functional screening and its in-house experimental platform. The resulting loop runs from molecular generation through experimental design, functional validation and model post-training to candidate development.

In this system, the laboratory is no longer a validation step that follows model output; it is a core part of how the model learns and improves. Models propose experimentally testable molecular designs and scientific hypotheses; the experimental platform generates high-quality functional data; and that data flows back into both the foundation model and project-specific models, driving continuous iteration into the next round of design and validation.

This Lab-in-the-Loop mechanism lets Aureka generate its own large-scale, information-dense functional experimental data for use in foundation model pre-training, reinforcement learning and project-specific post-training. Compared with development paths that rely mainly on public, static datasets, Aureka’s models receive experimental feedback from live drug discovery programs and evolve through a continuous design–validation–learning cycle — a flywheel in which data, models, experiments and drug assets reinforce one another.

Foundation Model Capability Confirmed by Third-Party Evaluation

That infrastructure produced AuraIDE, Aureka's own biological foundation model. Trained at scale on proprietary protein co-evolution data, it learns how protein sequence, structure, evolution and function relate to one another. On biomolecular structure prediction and de novo molecular design, it now ranks among the leaders.

Rather than a single-purpose algorithm, AuraIDE is built to transfer across multiple drug discovery programs through task adaptation and project-specific post-training. Its capabilities extend from protein structure modeling and molecular generation into biomolecular interaction modeling, function prediction and multi-objective optimization under complex design constraints.

OpenDDE, the open-source version of AuraIDE, ranks among the world’s leading open-source biomolecular models in independent third-party evaluations.

Together, the third-party evaluations and the wet-lab results indicate that Aureka's models lead on protein structure prediction and de novo design, and can translate that capability into measurable molecular function. Through continuous Lab-in-the-Loop feedback, they are moving from predicting biological structure toward generating biomolecules with intended function.