Serotiny is the market leader for designing therapeutic multi-domain proteins - from chimeric antigen receptors (CAR) to CRISPR gene editors, where the aim of the protein is to change the properties of a cell. Their platform relies on machine-guided variation to design in silico libraries of millions of protein designs and then test tens of thousands of them in vitro, and iterate to produce a high-value candidate. Versus the past state-of-the-art, Serotiny enables unbiased screening of large protein therapeutics in their native mammalian and therapeutic contexts. Unbiased screening for complex drugs has allowed the company to find new candidate combinations that are hard-if-not-impossible to discover with other approaches. By generating and intentionally structuring data that correlates primary amino acid with primary cell phenotype, the company's underlying platform is allowing Serotiny to move more quickly from idea to drug candidate.

At the end of the conversation, we discuss the long-term need for a common language in synthetic biology, building a world-class team, and the opportunities to standardize datasets in life sciences. Justin lays out a powerful framework for platform companies in drug development: going 0 to 1 to find a signal and invent a new candidate and then going from 1 to 100 and beyond by versioning the candidate to improve its therapeutic potential.