Ginkgo Bioworks and Deep Origin have entered into a 4.5‑year collaboration that will leverage Ginkgo’s Datapoints platform to generate high‑throughput perturbation‑response data across a broad array of cell and tissue types. The data will feed into Deep Origin’s PREDICTS (Pharmacological Research and Evaluation through Digital Integration and Clinical Trial Simulation) AI models, enabling more accurate predictions of cell‑type‑specific toxicity, transcriptomic changes, and other safety endpoints for small‑molecule drugs.
The partnership is funded by the U.S. Advanced Research Projects Agency for Health (ARPA‑H) through its Computational ADME‑Tox and Physiology Analysis for Safer Therapeutics (CATALYST) program. The CATALYST award can total up to $125 million over 4.5 years, and the collaboration represents a significant portion of that funding, underscoring federal support for Ginkgo’s data‑generation capabilities.
Ginkgo’s Datapoints platform automates the collection of DRUG‑seq transcriptomics, cell‑painting, and other high‑content readouts, producing data that are both high‑quality and high‑throughput. By feeding this data into Deep Origin’s AI models, the partnership aims to reduce late‑stage attrition by improving the predictive accuracy of preclinical safety assays, thereby accelerating the development of safer therapeutics.
Management emphasized the strategic fit of the collaboration. Jesse Dill, Senior Director at Ginkgo, said the partnership would “enable us to generate data characterizing cellular responses to drug treatments to support this effort.” George Pilitsis, Director at Ginkgo, added that the deal “pushes the boundaries of multi‑omics data generation and supports the vision to transform drug discovery with purpose‑built, relevant in‑vitro models coupled with cutting‑edge AI.” Deep Origin’s Natalie Ma noted that Ginkgo’s high‑throughput platforms “allow us to generate data at scale, enabling detailed modeling of a wide array of different cell types in tissues and organs.”
The partnership positions Ginkgo to deepen its footprint in the AI‑driven drug‑discovery space and to deliver safer therapeutics to the market. By combining Ginkgo’s automation and data‑generation strengths with Deep Origin’s modeling expertise, the collaboration is expected to accelerate the development of safer drugs and reduce late‑stage attrition, aligning with ARPA‑H’s broader goal of creating human‑based, AI‑enabled models that can predict drug safety and efficacy.
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