Relation and GSK expand collaboration on AI-driven drug discovery

Profluent announced a collaboration with BioMarin Pharmaceutical Inc. to design and optimize novel enzyme therapies for BioMarin's pipeline. Under the agreement, Profluent will apply its foundational AI models to generate novel potential drug candidates, and BioMarin will screen the results with the goal of eventually advancing the most promising designs. Profluent will design and optimize enzyme candidates using its suite of proprietary protein language models, which draw on the Profluent Protein Atlas, described as the largest protein sequence dataset in the world with more than 115 billion unique sequences. BioMarin will use Profluent's AI models to rapidly explore protein designs and identify candidates with desired activity and stability profiles, then advance the most promising into preclinical testing. Pooja Agarwal, Vice President and Head of Metabolic Conditions Research at BioMarin, said the collaboration pairs Profluent's de novo protein design approach with BioMarin's decades of enzyme therapy expertise, while Profluent CEO Ali Madani said the partnership aims to accelerate BioMarin's drug discovery pipeline.

Ginkgo Datapoints, an offering of Ginkgo Bioworks, and Apheris GmbH announced that the Antibody Developability Consortium has launched with AbbVie, argenx, Lundbeck, and Takeda as founding members. The industry collaboration aims to build the field's largest standardized antibody developability dataset, with each founding member contributing proprietary antibody sequences and Ginkgo Datapoints filling any remaining capacity from publicly available sources to reach 10,000 antibodies in total. Using Apheris' federated infrastructure, members can train, benchmark, and refine AI models on the full consortium dataset without exposing their raw proprietary sequences to other members, and they retain ownership of the sequences and assay data they contribute. Ginkgo Datapoints leads the scientific design and execution, including sequence selection, antibody production, and high-throughput wet-lab characterization, and trains a foundation antibody developability model within Apheris' secure environment. Charlotte Deane of the University of Oxford and Peter Tessier of the University of Michigan will provide independent scientific oversight, and the consortium's initial dataset is targeted for delivery to members by early 2027.