Our integrated platform conjoins

deep learning and high-throughput biology

designing antibody sequencesat massive scale, predicting their properties, validating via experiments, and self-improving iteratively. We balance the tradeoff between exploration and exploitation and search the sequence landscape, aiming for more precise epitope targeting and better developability after each iterative design cycle

Deep learning models of antibody-antigen interaction landscape

We train deep learning models to predict binding between antibodies and antigens. With ever-growing structural and functional data, our models become more precise and detailed.

Design and synthesis of antibody library

Given an antigen, we efficiently search the antibody sequence space to identify molecules that target an epitope with significant therapeutic implications. A library of up to millions of antibodies are then synthesized for testing.

High-throughput profiling of antibody properties

Using a variety of technologies, we obtain functional and biophysical measurements of individual antibodies in our library in a high-throughput manner.

Iterations towards optimized antibodies

By repeating the previous steps, we learn from the successes and failures of earlier experiments, train better models, design a new library of candidates, and further improve antibodies with desired properties.