Ginkgo Bioworks (NYSE: DNA) has announced a major expansion of its Datapoints platform to accelerate artificial intelligence applications in biologics drug discovery. The company unveiled two key initiatives: a strategic partnership with Apheris to launch the Antibody Developability Consortium and the first-ever AbDev AI Competition. These efforts aim to address critical bottlenecks in AI-driven drug development by building high-quality, collaborative data infrastructure. Drug discovery is increasingly hampered by incomplete or siloed datasets, limiting the effectiveness of AI models. While historical data remains valuable, the industry now demands fit-for-purpose, high-quality datasets to train next-generation AI systems. Ginkgo’s advanced lab automation capabilities allow it to generate such data rapidly, complementing legacy datasets with new, AI-optimized information. The Antibody Developability Consortium, led by Ginkgo Datapoints in collaboration with Apheris, targets a major challenge in biologics development: predicting and optimizing antibody properties early in research to improve clinical and commercial outcomes. The consortium uses a novel framework combining centralized dataset generation by Ginkgo with federated model training powered by Apheris’ secure, decentralized computing technology. This approach enables pharmaceutical companies to collaborate without exposing sensitive proprietary data, maintaining full intellectual property control. The initiative is currently enrolling member companies, with initial datasets and models for multiple antibody formats expected by 2026. Parallel to this, Ginkgo is launching the AbDev AI Competition, the first of its kind, to benchmark and standardize antibody developability modeling. Hosted on the Hugging Face platform, the competition runs until early November and offers up to $60,000 in prizes. It provides a transparent, standardized environment to evaluate AI models, identify performance gaps, and guide future research. Peter Tessier, professor at the University of Michigan and advisor to Ginkgo, emphasized that the lack of large, high-quality datasets has long hindered progress in antibody AI, making such competitions essential for advancing the field. These initiatives build on Ginkgo’s broader strategy to integrate AI with lab automation. The company has previously collaborated with Tangible Scientific and Inductive Bio to apply AI-driven, lab-in-the-loop workflows to small molecule drug discovery. John Androsavich, general manager of Ginkgo Datapoints, stated the company is creating collaborative frameworks and standards that will shape the future of AI in drug development, particularly for high-impact therapeutic areas like antibodies. Ginkgo Bioworks specializes in synthetic biology, offering R&D solutions in protein engineering, nucleic acid design, and cell-free systems. Its Datapoints division generates large-scale experimental datasets to power AI models, while Ginkgo Automation provides modular lab systems to increase research efficiency. The company also operates Ginkgo Biosecurity, focused on global biological threat detection and response. Apheris provides secure, on-premise AI tools for drug discovery, enabling pharma companies to run and customize models within their own IT environments. Its federated learning networks allow collaborative model training across organizations while preserving data sovereignty. The announcements underscore Ginkgo’s growing role in bridging biology, automation, and AI. While forward-looking statements in the release include risks related to market demand, regulatory changes, and project execution, the company’s dual focus on data generation and collaborative innovation positions it at the forefront of next-generation drug discovery. These efforts could significantly reduce development timelines and costs, accelerating the delivery of new therapies to patients.