Aureka Biotechnologies has secured a US$100 million Series B to accelerate Lab-in-the-Loop development, funding research and large-scale training of its biological foundation models to power an AI-native, closed-loop R&D infrastructure for drug discovery.

The company combines large-scale pre-training, post-training, AI agents, and experiments to build AI-for-Science infrastructure focused on life sciences, enabling models to learn biological rules and intervene in complex systems.

Its closed-loop Lab-in-the-Loop approach integrates proprietary single-cell functional screening and high-throughput validation to continuously generate training data and drive model improvement.

Investors Granite Asia, HighLight Capital, MPCi, and NRL Capital provide descriptions of their focus areas and potential strategic value as partners.

Background materials highlight these funds as key investors and partners, detailing each firm’s focus and assets in support of Aureka’s platform.

OpenDDE shows strong performance in antibody-antigen co-folding but trails AlphaFold 3 in protein-ligand co-folding, indicating domain-specific strengths and current limits.

A FAQ clarifies OpenDDE’s differences from AlphaFold 3, the rationale for releasing a best model openly, how Lab-in-the-Loop differentiates Aureka from competitors, and data-security considerations for Shanghai operations.

OpenDDE uses a widened Pairformer architecture with hundreds of millions of parameters across multiple blocks, trained on proprietary protein co-evolution data, requiring roughly 414,000 GPU-hours.

Aureka has formed strategic partnerships with multiple global pharmaceutical firms and reports tens of millions in revenue over the past two years from antibody programs, validating platform delivery and commercial potential.

These partnerships include work with major pharma players on differentiated antibody programs, including challenging targets such as GPCRs and dual-target antibodies.

Industry voices from Granite Asia, HLC, MPCi, and NRL Capital emphasize AI-native drug discovery’s transformative potential, the closed-loop infrastructure, and the value of an open-source ecosystem.

The competitive landscape is capital-intensive and multi-lane, with players like Earendil Labs and Isomorphic Labs underscoring that durable advantages hinge on data, end-to-end platforms, and scale.