By integrating high-throughput, 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 design, build, test and learn enables us to develop a pipeline that can exponentially accelerate therapeutic design.
By unlocking the design principles of immunity, we can transform the immunotherapeutic design from an empirical and trial-and-error approach to truly rational engineering practice.
Single-cell digital biology
Decode nature’s design principle
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 design, build, test and learn cycle. As the explosion of structural and functional in-house data increases, our engines become more accurate and rational.
We are developing technologies that can mine the functional sequence space using libraries with unprecedented high diversity to enable discovery and data generation. Our pipeline also integrates high-throughput protein synthesis and characterization to evaluate the AI-designed therapeutic candidates at scale.
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