By integrating high-throughput autonomous evolution, single-cell-based functional screening and AI-based drug design and development, each experiment can interrogate millions of therapeutic candidates and generate large-scale, multi-metric data associated with each candidate with respect to function, binding, and developability. The efficient iteration among generate, test, learn and optimize enables us to develop a pipeline that can exponentially accelerate therapeutic design.
By exploring and learning from parallel protein evolution experiments, Aureka's AI platform can decode the language of life and transform therapeutic design from an empirical and trial-and-error approach to truly rational engineering practice.
Single-cell digital biology
functional screening
evolution
Decode the language of life
We are developing scalable evolution technologies that can mine the protein sequence, structure and function space using libraries with unprecedented high diversity to enable discovery and data generation. Our data-driven protein language model can therefore perform accurate multi-parameter and de novo design at scale.
Our proprietary high-resolution and high-throughput single-cell functional screening platform enables us to significantly increase discovery efficiency while generating indexable, content rich data for each cell. This results in abundant, high-quality training sets for our deep reinforcement learning techniques.
We employed geometric-aware protein language model and reinforcement learning to drive the generate, test, learn and optimize cycle. With unprecedented data iteration efficiency, our AI solutions allow accurate and rational therapeutic design with desirable attributes.
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