Ginkgo Bioworks, through its Datapoints division, has partnered with Apheris to establish the Antibody Developability Consortium. This initiative aims to enhance the application of artificial intelligence in biologics drug discovery by addressing key challenges in antibody developability.
Addressing Challenges in Antibody Developability
The development of therapeutic antibodies often encounters hurdles related to their physicochemical properties, which can impact their efficacy and manufacturability. Early prediction and optimization of these properties are crucial to ensure the success of biologic drugs in clinical and commercial stages. The consortium seeks to tackle these challenges by creating high-quality, fit-for-purpose datasets that can train next-generation AI models, thereby improving the predictability and optimization of antibody properties.
Collaborative Approach to Data Sharing
Apheris will contribute its federated computing infrastructure to the consortium, enabling secure collaboration among members on sensitive data while maintaining full ownership and control. This approach aims to overcome the limitations of siloed datasets, which have historically hindered the effectiveness of AI in drug discovery. By facilitating the sharing of data across organizations, the consortium intends to accelerate the development of AI models that can predict antibody developability with greater accuracy.
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Launch of the AbDev AI Competition
In conjunction with the consortium, Ginkgo is launching the AbDev AI Competition, the first of its kind in the field. This competition is designed to assess the current state of antibody developability modeling and establish widely accepted standards for performance and evaluation. By providing a transparent, structured environment for testing models, the competition will help identify areas of strength and highlight where new methods and datasets are most urgently needed.
Implications for AI and Biotech Professionals
For professionals in AI, life sciences, and biotech, the establishment of the Antibody Developability Consortium represents a significant step forward in the integration of AI into biologics drug discovery. The collaborative efforts to create high-quality datasets and standardized evaluation methods are expected to enhance the efficiency and effectiveness of AI models, leading to more successful and timely development of therapeutic antibodies.
