Ginkgo Datapoints and Apheris launched an Antibody Developability Consortium to help pharmaceutical and biotech teams predict manufacturability and developability risks earlier in discovery. The initiative aims to build a standardized dataset by pooling proprietary antibody sequencing from founding members including AbbVie, argenx, Lundbeck and Takeda. The consortium’s stated goal is to reach 10,000 antibodies, supplementing with publicly available sources while using federated infrastructure to keep sequences private. The dataset is designed to support machine-learning models that forecast barriers that can derail candidates during manufacturing, formulation and clinical advancement. Participants framed antibody developability as a field-wide constraint where “convenience” datasets may not reflect the controlled measurements needed for predictive modeling, particularly for complex targets such as CNS programs. This is a notable step toward formalizing shared data infrastructure in antibody therapeutics, with potential downstream impact on candidate selection and development timelines.