Ginkgo Bioworks Launches New Protein LLM and Model API Built on Google Cloud Technology
- Protein large language model (LLM) designed to help enterprises accelerate drug development coming to Google Cloud's Vertex AI Model Garden soon; one of the first-of-its-kind in the industry
- Model API programmable interface now available for individual scientists and researchers to quickly test and advance work
These new offerings demonstrate how Ginkgo is enabling the life sciences industry in new ways, helping them improve and accelerate the drug development process.
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Protein LLM for individual researchers and enterprise companies: Built on Vertex AI in collaboration with
Google Cloud Consulting and trained on Ginkgo's extensive proprietary dataset, this and future LLMs empower companies to generate novel insights and accelerate the discovery of new therapeutics. By harnessing the power of AI to analyze and understand complex protein structures and interactions, researchers and enterprises can streamline their research pipelines, optimize lead identification, and ultimately bring life-saving medicines to market faster and more efficiently. Building on models that learn from Ginkgo's private data, unavailable to the public, can enable companies to unlock hidden patterns and potential therapeutic targets that would otherwise remain elusive. - Open API for scientists and researchers: With this programmer-friendly ultra-low cost API, Ginkgo is making its internally-developed AI tools available to anyone. The interface provides an easy and scalable way to access sophisticated models trained on protein and DNA data, starting with its first release: a machine learning model trained on a proprietary Ginkgo dataset. (Read more about Ginkgo's first model — ginkgo-AA-0-650m, a large-scale model trained on 2+ billion proprietary Ginkgo protein sequences — here.)
Ginkgo has a multitude of models under development, spanning machine learning methods like language modeling and diffusion for conditional design. Ginkgo's first protein language model release will support two use-cases:
- Generation via Masked Language Modeling: given a sequence of amino acids with one or more <mask> tokens, the model will complete the sequence.
- Embedding calculation: Calculate the final hidden layer of the trained model to extract valuable representations for downstream tasks. To begin, Ginkgo's model returns the mean-pooled representation across the length axis.
Over the next year, Ginkgo will roll out more models and expand the API's capabilities, building a robust suite of tools that will enable you to solve complex problems in drug discovery, synthetic biology, genomics, and more using the latest machine learning methods. Access the portal today and be among the first to explore our new API.
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Forward-Looking Statements of
This press release contains certain forward-looking statements within the meaning of the federal securities laws, including statements regarding the capabilities and potential success of the partnership, Ginkgo's model API and Ginkgo's cell programming platform. These forward-looking statements generally are identified by the words "believe," "can," "project," "potential," "expect," "anticipate," "estimate," "intend," "strategy," "future," "opportunity," "plan," "may," "should," "will," "would," "will be," "will continue," "will likely result," and similar expressions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this press release, including but not limited to: (i) volatility in the price of Ginkgo's securities due to a variety of factors, including changes in the competitive and highly regulated industries in which Ginkgo operates and plans to operate, variations in performance across competitors, and changes in laws and regulations affecting Ginkgo's business, (ii) the ability to implement business plans, forecasts, and other expectations, and to identify and realize additional business opportunities, (iii) the risk of downturns in demand for products using synthetic biology, (iv) the uncertainty regarding the demand for passive monitoring programs and biosecurity services, (v) changes to the biosecurity industry, including due to advancements in technology, emerging competition and evolution in industry demands, standards and regulations, (vi) the outcome of any pending or potential legal proceedings against Ginkgo, (vii) our ability to realize the expected benefits from and the success of our Foundry platform programs, (viii) our ability to successfully develop engineered cells, bioprocesses, data packages or other deliverables, and (ix) the product development or commercialization success of our customers. The foregoing list of factors is not exhaustive. You should carefully consider the foregoing factors and the other risks and uncertainties described in the "Risk Factors" section of Ginkgo's annual report on Form 10-K filed with the
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