Aureka Biotechnologies Raises $100M to Advance AI Drug Discovery
Aureka Biotechnologies announced the completion of a US$100 million Series B financing round on August 10, 2026. Granite Asia provided the first tranche as the sole investor, while a leading strategic investor led a subsequent tranche that included participation from HighLight Capital (HLC) and additional investments from existing shareholders such as MPCi and NRL Capital. The latest financing brings Aureka’s total capital raised to nearly US$200 million.
The company plans to allocate the new capital primarily to research and large-scale training of its next generation of biological foundation models. These efforts are intended to enhance performance in areas including de novo molecular design, biological structure modeling and biological function prediction. Aureka will also expand Lab-in-the-Loop, its experiment-driven feedback platform, to create a stronger connection between its foundation models and proprietary single-cell functional screening, high-throughput experimental validation and drug development capabilities.
Having established an AI-native, closed-loop R&D infrastructure, Aureka is now concentrating on strengthening the intelligence layer at the center of that infrastructure: its foundation models. The company combines large-scale pre-training, program-specific post-training, AI agents and high-throughput experimentation to create an AI-for-Science platform for life sciences.
Rather than limiting AI to individual drug discovery tasks, Aureka aims to develop models capable of learning biological principles and using that understanding to interpret, generate, predict and ultimately influence complex biological systems.
As foundation models increasingly merge with automated R&D, Aureka is pursuing a broader ambition: moving beyond the use of AI as a tool for improving individual stages of drug discovery toward AI systems capable of modeling living systems themselves. The shift could expand both the technological capabilities and commercial opportunities of AI-powered drug discovery.
Closed-Loop AI Infrastructure Strengthens the Model Intelligence Layer
Established in 2023, Aureka Biotechnologies is an AI-native TechBio company focused on biological foundation models and the closed-loop infrastructure supporting them. Its platform brings together AI models, autonomous agents, digital biology and experimental technologies with the goal of transforming the drug discovery process from end to end.
Biological discovery cannot be driven by computation alone. Models must continually receive information from real-world experiments. Improving large biological models therefore requires more than additional computing resources, better algorithms or new model architectures. It also requires reliable experimental data that captures molecular behavior and an experimental environment capable of repeatedly testing hypotheses, identifying errors and incorporating new findings into subsequent model iterations.
Aureka has positioned Lab-in-the-Loop as a central component of this development process. The system combines AI agents, high-throughput digital biology, proprietary single-cell functional screening and internal experimental capabilities. This creates an integrated workflow spanning molecular generation, experimental planning, functional testing, model post-training and candidate development.
Under this approach, the laboratory is not simply used to verify computational predictions after they have been produced. Instead, experimentation becomes part of the learning process itself. Models generate molecular designs and scientific hypotheses that can be experimentally tested. The company's experimental systems then produce functional data, which is fed back into both its foundation models and program-specific models. The resulting information supports another cycle of design, testing and improvement.
This approach also allows Aureka to produce large volumes of information-rich functional experimental data that can support foundation-model pre-training, reinforcement learning and program-specific post-training. Unlike approaches built predominantly around publicly available and static datasets, Aureka's models can receive continuous experimental feedback from active drug discovery programs.
The result is a recurring cycle in which data, models, experiments and emerging drug candidates continuously strengthen one another.
Independent Evaluation Supports Foundation Model Performance
Aureka's infrastructure has produced AuraIDE, the company's biological foundation model. Trained extensively using proprietary protein co-evolution data, the model is designed to capture relationships among protein sequence, structure, evolutionary history and biological function. The company reports that AuraIDE has achieved leading performance in areas including biomolecular structure prediction and de novo molecular design.
AuraIDE is designed as a general biological foundation model rather than a tool dedicated to a single application. Through task adaptation and program-specific post-training, its capabilities can be transferred across different drug discovery initiatives. These capabilities include protein structure prediction, molecular generation, biomolecular interaction modeling, functional prediction and optimization involving multiple design objectives and constraints.
Aureka's open-source model, OpenDDE, has also performed strongly in independent third-party assessments, ranking among the leading open-source biomolecular models evaluated to date.
Together, independent assessments and experimental results suggest that Aureka's models can perform strongly in protein structure prediction and de novo molecular design while also translating computational predictions into measurable biological activity. Through repeated Lab-in-the-Loop feedback, the company is working to move beyond predicting biological structures toward designing biomolecules with specific intended functions.
Commercial Applications Convert Model Intelligence into Drug Assets
Aureka is combining its biological foundation models, proprietary single-cell functional screening technology and program-specific post-training to develop differentiated antibodies at scale. The company is targeting areas that can be difficult for conventional discovery methods, including challenging targets such as G protein-coupled receptors (GPCRs) and dual-target antibodies designed to interact with two targets through a single molecule.
For individual programs, Aureka adapts its foundation models to the specific biological mechanism, functional characteristics and developability requirements associated with the target. This process transforms broad biological knowledge into a specialized model tailored to a particular discovery challenge.
AI agents can then coordinate activities spanning target analysis, molecular generation, computational evaluation, experimental planning and interpretation of experimental results. Data generated during those experiments is subsequently incorporated into the relevant program model, creating another cycle of refinement.
Aureka's end-to-end agentic R&D infrastructure links molecular generation with developability analysis, experimental testing, feedback and candidate advancement. By connecting these capabilities, the company aims to accelerate the transition from scientific hypotheses and computational models to drug candidates suitable for development.
The platform supports both Aureka's internal pipeline and collaborations with external partners. The company has established strategic relationships with several major global pharmaceutical companies to advance differentiated antibody therapeutics. It also reports generating tens of millions of dollars in revenue during the past two years, which it views as evidence of the platform's ability to deliver at scale in real-world drug discovery programs.
Moving Beyond Efficiency Toward a Biological World Model
“When advanced biological foundation models are combined with R&D infrastructure capable of operating at scale, the objective changes from optimizing a single stage of drug discovery to creating a new generation of discovery engines capable of understanding, generating and predicting biological systems,” said Dr. Weian Zhao, Founder and Chief Executive Officer of Aureka Biotechnologies. “This represents an important milestone in our longer-term effort to develop a biological world model.”
Aureka's longer-term vision extends beyond predicting static molecular structures. The company aims to develop systems that can model molecular interactions, anticipate the consequences of molecular engineering and design decisions, and enable AI agents to autonomously plan, execute and refine drug discovery workflows.
The latest financing will support further development of the relationship between Aureka's biological foundation models and its closed-loop AI-native infrastructure. The company intends to accelerate the testing and real-world application of its models in active drug discovery programs while expanding the role of generative AI in antibody development.
Investor Perspectives
Granite Asia — Yinghui Kuang
Granite Asia views AI-driven drug discovery as entering a stage in which progress will depend increasingly on the interaction among data, models and experiments rather than on isolated improvements in model performance. The firm believes Aureka's AI-native R&D infrastructure gives it the ability to continuously generate high-quality experimental data, improve its models and translate those capabilities into drug candidates. Granite Asia is optimistic about Aureka's long-term potential in generative antibody design and its prospects for international expansion.
HighLight Capital (HLC)
HighLight Capital said Aureka's integration of generative AI with high-throughput wet-lab capabilities could contribute to a significant change in biologics R&D. The firm highlighted the team's technical expertise in AI-enabled drug development and the efficiency of its closed-loop approach. HLC also expressed its intention to continue supporting Aureka's pipeline development, technological advancement and international application of AI in drug innovation.
MPCi — Yuye Wang
MPCi described Aureka as a company it supported at an early stage and continues to have confidence in. It highlighted OpenDDE, Aureka's open-source all-atom model, as evidence of the team's technical depth and early recognition of AI's potential in drug discovery. MPCi also pointed to Aureka's differentiated pipeline and expressed confidence that the company's combination of technical innovation and strategic execution can overcome persistent challenges in drug development and contribute to broader industry transformation.
NRL Capital
NRL Capital said Aureka is helping establish a new infrastructure model for AI-powered drug discovery. The firm views the open-source release of OpenDDE as an important step toward building a broader platform that connects computational models with highly automated, high-throughput laboratory experimentation.
NRL Capital also emphasized Aureka's ambition to establish a globally accessible ecosystem for AI-native drug discovery and praised Dr. Weian Zhao and his team for pursuing an open-source approach. The investor believes that tightly integrating computational and experimental workflows can deliver substantial improvements in antibody design efficiency. NRL Capital has made an additional investment in the latest financing round and plans to continue supporting Aureka's international expansion and efforts to realize the broader value of its AI infrastructure.
The company plans to allocate the new capital primarily to research and large-scale training of its next generation of biological foundation models. These efforts are intended to enhance performance in areas including de novo molecular design, biological structure modeling and biological function prediction. Aureka will also expand Lab-in-the-Loop, its experiment-driven feedback platform, to create a stronger connection between its foundation models and proprietary single-cell functional screening, high-throughput experimental validation and drug development capabilities.
Having established an AI-native, closed-loop R&D infrastructure, Aureka is now concentrating on strengthening the intelligence layer at the center of that infrastructure: its foundation models. The company combines large-scale pre-training, program-specific post-training, AI agents and high-throughput experimentation to create an AI-for-Science platform for life sciences.
Rather than limiting AI to individual drug discovery tasks, Aureka aims to develop models capable of learning biological principles and using that understanding to interpret, generate, predict and ultimately influence complex biological systems.
As foundation models increasingly merge with automated R&D, Aureka is pursuing a broader ambition: moving beyond the use of AI as a tool for improving individual stages of drug discovery toward AI systems capable of modeling living systems themselves. The shift could expand both the technological capabilities and commercial opportunities of AI-powered drug discovery.
Closed-Loop AI Infrastructure Strengthens the Model Intelligence Layer
Established in 2023, Aureka Biotechnologies is an AI-native TechBio company focused on biological foundation models and the closed-loop infrastructure supporting them. Its platform brings together AI models, autonomous agents, digital biology and experimental technologies with the goal of transforming the drug discovery process from end to end.
Biological discovery cannot be driven by computation alone. Models must continually receive information from real-world experiments. Improving large biological models therefore requires more than additional computing resources, better algorithms or new model architectures. It also requires reliable experimental data that captures molecular behavior and an experimental environment capable of repeatedly testing hypotheses, identifying errors and incorporating new findings into subsequent model iterations.
Aureka has positioned Lab-in-the-Loop as a central component of this development process. The system combines AI agents, high-throughput digital biology, proprietary single-cell functional screening and internal experimental capabilities. This creates an integrated workflow spanning molecular generation, experimental planning, functional testing, model post-training and candidate development.
Under this approach, the laboratory is not simply used to verify computational predictions after they have been produced. Instead, experimentation becomes part of the learning process itself. Models generate molecular designs and scientific hypotheses that can be experimentally tested. The company's experimental systems then produce functional data, which is fed back into both its foundation models and program-specific models. The resulting information supports another cycle of design, testing and improvement.
This approach also allows Aureka to produce large volumes of information-rich functional experimental data that can support foundation-model pre-training, reinforcement learning and program-specific post-training. Unlike approaches built predominantly around publicly available and static datasets, Aureka's models can receive continuous experimental feedback from active drug discovery programs.
The result is a recurring cycle in which data, models, experiments and emerging drug candidates continuously strengthen one another.
Independent Evaluation Supports Foundation Model Performance
Aureka's infrastructure has produced AuraIDE, the company's biological foundation model. Trained extensively using proprietary protein co-evolution data, the model is designed to capture relationships among protein sequence, structure, evolutionary history and biological function. The company reports that AuraIDE has achieved leading performance in areas including biomolecular structure prediction and de novo molecular design.
AuraIDE is designed as a general biological foundation model rather than a tool dedicated to a single application. Through task adaptation and program-specific post-training, its capabilities can be transferred across different drug discovery initiatives. These capabilities include protein structure prediction, molecular generation, biomolecular interaction modeling, functional prediction and optimization involving multiple design objectives and constraints.
Aureka's open-source model, OpenDDE, has also performed strongly in independent third-party assessments, ranking among the leading open-source biomolecular models evaluated to date.
Together, independent assessments and experimental results suggest that Aureka's models can perform strongly in protein structure prediction and de novo molecular design while also translating computational predictions into measurable biological activity. Through repeated Lab-in-the-Loop feedback, the company is working to move beyond predicting biological structures toward designing biomolecules with specific intended functions.
Commercial Applications Convert Model Intelligence into Drug Assets
Aureka is combining its biological foundation models, proprietary single-cell functional screening technology and program-specific post-training to develop differentiated antibodies at scale. The company is targeting areas that can be difficult for conventional discovery methods, including challenging targets such as G protein-coupled receptors (GPCRs) and dual-target antibodies designed to interact with two targets through a single molecule.
For individual programs, Aureka adapts its foundation models to the specific biological mechanism, functional characteristics and developability requirements associated with the target. This process transforms broad biological knowledge into a specialized model tailored to a particular discovery challenge.
AI agents can then coordinate activities spanning target analysis, molecular generation, computational evaluation, experimental planning and interpretation of experimental results. Data generated during those experiments is subsequently incorporated into the relevant program model, creating another cycle of refinement.
Aureka's end-to-end agentic R&D infrastructure links molecular generation with developability analysis, experimental testing, feedback and candidate advancement. By connecting these capabilities, the company aims to accelerate the transition from scientific hypotheses and computational models to drug candidates suitable for development.
The platform supports both Aureka's internal pipeline and collaborations with external partners. The company has established strategic relationships with several major global pharmaceutical companies to advance differentiated antibody therapeutics. It also reports generating tens of millions of dollars in revenue during the past two years, which it views as evidence of the platform's ability to deliver at scale in real-world drug discovery programs.
Moving Beyond Efficiency Toward a Biological World Model
“When advanced biological foundation models are combined with R&D infrastructure capable of operating at scale, the objective changes from optimizing a single stage of drug discovery to creating a new generation of discovery engines capable of understanding, generating and predicting biological systems,” said Dr. Weian Zhao, Founder and Chief Executive Officer of Aureka Biotechnologies. “This represents an important milestone in our longer-term effort to develop a biological world model.”
Aureka's longer-term vision extends beyond predicting static molecular structures. The company aims to develop systems that can model molecular interactions, anticipate the consequences of molecular engineering and design decisions, and enable AI agents to autonomously plan, execute and refine drug discovery workflows.
The latest financing will support further development of the relationship between Aureka's biological foundation models and its closed-loop AI-native infrastructure. The company intends to accelerate the testing and real-world application of its models in active drug discovery programs while expanding the role of generative AI in antibody development.
Investor Perspectives
Granite Asia — Yinghui Kuang
Granite Asia views AI-driven drug discovery as entering a stage in which progress will depend increasingly on the interaction among data, models and experiments rather than on isolated improvements in model performance. The firm believes Aureka's AI-native R&D infrastructure gives it the ability to continuously generate high-quality experimental data, improve its models and translate those capabilities into drug candidates. Granite Asia is optimistic about Aureka's long-term potential in generative antibody design and its prospects for international expansion.
HighLight Capital (HLC)
HighLight Capital said Aureka's integration of generative AI with high-throughput wet-lab capabilities could contribute to a significant change in biologics R&D. The firm highlighted the team's technical expertise in AI-enabled drug development and the efficiency of its closed-loop approach. HLC also expressed its intention to continue supporting Aureka's pipeline development, technological advancement and international application of AI in drug innovation.
MPCi — Yuye Wang
MPCi described Aureka as a company it supported at an early stage and continues to have confidence in. It highlighted OpenDDE, Aureka's open-source all-atom model, as evidence of the team's technical depth and early recognition of AI's potential in drug discovery. MPCi also pointed to Aureka's differentiated pipeline and expressed confidence that the company's combination of technical innovation and strategic execution can overcome persistent challenges in drug development and contribute to broader industry transformation.
NRL Capital
NRL Capital said Aureka is helping establish a new infrastructure model for AI-powered drug discovery. The firm views the open-source release of OpenDDE as an important step toward building a broader platform that connects computational models with highly automated, high-throughput laboratory experimentation.
NRL Capital also emphasized Aureka's ambition to establish a globally accessible ecosystem for AI-native drug discovery and praised Dr. Weian Zhao and his team for pursuing an open-source approach. The investor believes that tightly integrating computational and experimental workflows can deliver substantial improvements in antibody design efficiency. NRL Capital has made an additional investment in the latest financing round and plans to continue supporting Aureka's international expansion and efforts to realize the broader value of its AI infrastructure.
Companies