Ginkgo to Run AbDev AI Competition

Ginkgo Bioworks (NYSE: DNA) today announced a series of new initiatives from its Datapoints offering to accelerate the application of artificial intelligence in biologics drug discovery. These include a strategic partnership with Apheris to launch the Antibody Developability Consortium and, separately, the AbDev AI Competition. Together, these efforts aim to position Ginkgo Datapoints as a leader in creating the data infrastructure and collaborative frameworks needed to advance antibody AI.

Tackling Foundational Challenges in Drug Development

Drug developers face rising costs and longer timelines. AI is positioned to reduce both but is hindered in part by incomplete or siloed datasets, which limits their effectiveness for training AI. While historical datasets remain valuable, the industry now requires higher-quality, fit-for-purpose data to train next-generation models. Through its advanced lab automation, we believe Ginkgo can now generate such datasets in a fraction of the time—helping to complement legacy program data with new data designed for improving AI applications.

Antibody Developability Consortium

The Antibody Developability Consortium, led by Ginkgo Datapoints in partnership with Apheris, aims to address one of the most significant challenges in biologics development: predicting and optimizing antibody properties early in R&D to better ensure downstream clinical and commercial success.

• Ginkgo Datapoints will contribute its AI/ML and high-throughput experimental capabilities, creating purpose-built datasets for improved model training
• Apheris will provide federated computing infrastructure that enables members to collaborate securely on sensitive data while maintaining full ownership and control

“The future of AI in drug discovery depends on creating environments where companies can collaborate without compromising their most valuable data,” said Robin Röhm, CEO and cofounder of Apheris. “We are successfully doing this with the AI Structural Biology (AISB) consortium, another cross-industry initiative powered by Apheris, and now with this new consortium with Ginkgo, we’re bringing federated learning directly into antibody R&D, making critical datasets usable in ways that were never before possible.”

By combining centralized dataset generation with federated model training, the consortium establishes a new framework for collaboration across the industry. The approach blends high-quality diverse datasets generated by Ginkgo with secure access to distributed partner datasets through Apheris’ federated computing technology.

The consortium is currently enrolling member companies with initial datasets and models for multiple antibody formats expected in 2026.

AbDev AI Competition: Establishing Standards for the Field

In parallel, Ginkgo is launching the first ever AbDev AI Competition. The competition is designed to measure the current state of antibody developability modeling and to create widely accepted standards for performance and evaluation. By providing a transparent, structured environment for testing models, the competition will help highlight areas of strength in the field and identify where new methods and datasets are most urgently needed.

“One of the biggest barriers in antibody AI has been the lack of large, high-quality datasets on developability. Consortia and competitions like these are a crucial step toward closing that gap—creating the shared data and benchmarks we need to advance predictive models across the field,” said Peter Tessier, professor of pharmaceutical sciences and chemical engineering at the University of Michigan and paid advisor to Ginkgo Datapoints.

The AI competition, hosted on the Hugging Face platform, runs now until early November, when winners will be announced with up to $60,000 in prize values.

Extending Ginkgo’s Commitment Across Modalities

These efforts build on Ginkgo Datapoints’ recent collaboration with Tangible Scientific and Inductive Bio to advance small molecule drug discovery through AI-driven, lab-in-the-loop workflows.

“We are creating collaborative frameworks and helping to establish the standards that will shape the future of AI for the most important areas of drug development,” said John Androsavich, general manager of Ginkgo Datapoints.

Antibodies represent one of the most successful therapeutic modalities and are a multibillion-dollar market. The Antibody Developability Consortium underscores Ginkgo’s commitment to scaling its biologics capabilities and providing the data infrastructure needed to accelerate R&D across major drug classes, creating value for partners and the broader ecosystem.

About Ginkgo Bioworks

Ginkgo Bioworks builds the tools that make biology easier to engineer for everyone. Ginkgo R&D Solutions delivers customizable R&D packages—such as protein engineering, nucleic acid design, and cell-free systems—giving partners a comprehensive way to accelerate innovation across therapeutics, diagnostics, & manufacturing. Ginkgo Automation sells modular, integrated laboratory automation so scientists can spend their days planning and analyzing experiments rather than pipetting in the lab. Ginkgo Datapoints uses Ginkgo’s in-house automation to generate the large lab datasets to power your AI models. Ginkgo Biosecurity is building and deploying the next-generation infrastructure and technologies that global leaders need to predict, detect, and respond to a wide variety of biological threats. For more information, visit ginkgobioworks.com and ginkgobiosecurity.com, read our blog, or follow us on social media channels such as X (@Ginkgo and @Ginkgo_Biosec), Instagram (@GinkgoBioworks), Threads (@GinkgoBioworks), or LinkedIn.

About Apheris

Apheris delivers enterprise-grade AI applications for drug discovery, designed for pharma companies to run and customize securely within their own IT environments. By keeping all data local, organizations maintain full sovereignty over IP-sensitive assets. These local deployments also serve as the foundation for Apheris-hosted federated data networks, where pharma companies collaboratively train and benchmark models on proprietary datasets—unlocking more robust and generalizable models for drug discovery. For more information, visit apheris.com or follow us on LinkedIn. For additional information, please contact: press@apheris.com.

Forward-Looking Statements of Ginkgo Bioworks

This press release contains certain forward-looking statements within the meaning of the federal securities laws, including statements regarding the capabilities and potential success of the partnership and Ginkgo's cell programming platform. These forward-looking statements generally are identified by the words "believe," "can," "project," "potential," "expect," "anticipate," "estimate," "intend," "strategy," "future," "opportunity," "plan," "may," "should," "will," "would," "will be," "will continue," "will likely result," and similar expressions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this press release, including but not limited to: (i) our ability to realize near-term and long-term cost savings associated with our site consolidation plans, including the ability to terminate leases or find sub-lease tenants for unused facilities, (ii) volatility in the price of Ginkgo's securities due to a variety of factors, including changes in the competitive and highly regulated industries in which Ginkgo operates and plans to operate, variations in performance across competitors, and changes in laws and regulations affecting Ginkgo's business, (iii) the ability to implement business plans, forecasts, and other expectations, and to identify and realize additional business opportunities, including with respect to our solutions and tools offerings, (iv) the risk of downturns in demand for products using synthetic biology, (v) the uncertainty regarding the demand for passive monitoring programs and biosecurity services, (vi) changes to the biosecurity industry, including due to advancements in technology, emerging competition and evolution in industry demands, standards and regulations, (vii) the outcome of any pending or potential legal proceedings against Ginkgo, (viii) our ability to realize the expected benefits from and the success of our Foundry platform programs and Codebase assets, (ix) our ability to successfully develop engineered cells, bioprocesses, data packages or other deliverables, (x) the product development, production or manufacturing success of our customers, (xi) our exposure to the volatility and liquidity risks inherent in holding equity interests in other operating companies and other non-cash consideration we may receive for our services, (xii) the potential negative impact on our business of our restructuring or the failure to realize the anticipated savings associated therewith and (xiii) the uncertainty regarding government budgetary priorities and funding allocated to government agencies. The foregoing list of factors is not exhaustive. You should carefully consider the foregoing factors and the other risks and uncertainties described in the "Risk Factors" section of Ginkgo's annual report on Form 10-K filed with the U.S. Securities and Exchange Commission (the "SEC") on February 25, 2025 and other documents filed by Ginkgo from time to time with the SEC. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and Ginkgo assumes no obligation and does not intend to update or revise these forward-looking statements, whether as a result of new information, future events, or otherwise. Ginkgo does not give any assurance that it will achieve its expectations.

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