Ginkgo Datapoints, an offering of Ginkgo Bioworks, and Apheris GmbH today announced that the Antibody Developability Consortium has kicked-off with its founding members. The Consortium is a new industry collaboration designed to help pharmaceutical and biotech companies predict manufacturability and developability risks earlierby building the field’s largest standardized antibody developability dataset. The founding members of the consortium include AbbVie, argenx, Lundbeck, and Takeda, and it remains open to additional pharmaceutical and biotech companies.

“Pooling standardized developability data across the industry can create stronger predictive models than any one company could build alone” said Yves Fomekong Nanfack, Head of AI/ML Research at Takeda. “As we advance Takeda Research’s ambition to become an AI-native discovery organization, this capability can help identify promising antibody candidates earlier, inform better development decisions and bring new therapies to patients faster.”

Developability challenges can prevent otherwise promising antibody candidates from progressing efficiently toward patients. Antibody developability, encompass the biophysical properties that influence whether a candidate antibody can be manufactured, formulated, and successfully advanced into a clinical product. Predicting these barriers early could support more informed candidate selection and reduce avoidable development time and investment. However, existing predictive models have been limited by small, fragmented, and inconsistent datasets, while even large internal datasets are constrained in sequence diversity.

The Antibody Developability Consortium was created to address this gap by unifying efforts to produce a standardized, purpose-built, diverse dataset and corresponding AI models at scale.

Each founding member will contribute proprietary antibody sequences, with Ginkgo Datapoints filling any remaining capacity from publicly available sources to reach 10,000 antibodies in total. All members benefit from the rich diversity of antibody sequences sourced collectively from members and public sources. Using Apheris’s infrastructure, members can train, benchmark, and refine AI models using the full consortium dataset, without raw proprietary sequences being exposed to other members.

Erwin Pannecoucke, Principal Scientist Discovery, argenx, says: “Predictive developability models can significantly accelerate discovery and development. The Consortium’s extensive antibody dataset and federated design enable every partner to learn together and ultimately bring better medicines to patients faster.”

The consortium is designed so that members can develop models trained on the full consortium dataset, and apply resulting models and their own fine-tuned derivatives internally while retaining ownership of the proprietary sequences and assay data they contribute.

Ginkgo Datapoints leads the scientific design and execution of the consortium: designing the sequence selection approach, overseeing antibody production, and conducting high-throughput wet-lab characterization across core developability endpoints. Ginkgo also trains a foundation antibody developability model on the resulting dataset within Apheris’s secure environment. Apheris’ federated infrastructure delivers the foundation model into each member’s environment for fine-tuning on proprietary data, as the basis for developing new models. The same infrastructure runs Ginkgo’s diversity algorithm for sequence selection, allows members to train their own models using consortium data, and protects confidentiality of proprietary sequences contributed by individual memebers. Charlotte Deane, Professor of Structural Bioinformatics at the University of Oxford and Peter Tessier, Professor of Pharmaceutical Sciences and Chemical Engineering at the University of Michigan, will provide independent scientific oversight. Together, the consortium provides pharmaceutical partners with  a broader portfolio-level developability capability without having to build the data or infrastructure themselves.

The consortium’s initial dataset is targeted for delivery to members by early 2027. The consortium will also explore the addition of more complex antibody formats over time to enable new drug classes and other key properties that enable early predictions of which drugs will succeed or fail.

“For AI to impact developability decisions in a drug program, it has to perform on a pharma’s own molecules,” says Robin Röhm, CEO and co-founder of Apheris. “The Antibody Developability Consortium delivers the largest standardized antibody dataset and the foundation model trained on it. Apheris’ federated infrastructure brings that model to each member and lets them fine-tune it on their proprietary molecules inside their own environment.”

“We are building the largest, most standardized antibody developability dataset the industry has ever seen, along with the predictive models trained on it,” says Rich Cohen, Senior Director at Ginkgo Datapoints. “Ginkgo Datapoints brings the lab data generation scale, the diverse sequence selection expertise, and the published modelling track record needed to lead this initiative.”

“At argenx, collaboration is central to how we innovate. Through our Immunology Innovation Program, we combine deep disease biology expertise with antibody engineering to advance new medicines for patients with high unmet need,” said

“This consortium represents an important step forward in building predictive models for antibody developability by creating datasets that are designed for machine learning, addressing limitations associated with convenience datasets,” said Athena Hadjixenofontos, Director of Data Science, Head of AI in Biotherapeutics and Genetic Medicine at AbbVie. “Federated infrastructure enables participants to contribute data while keeping proprietary sequences private. These capabilities could meaningfully accelerate antibody discovery and help advance new medicines for patients.”

For more information, visit https://datapoints.ginkgo.bio/.