Continuous Variational Synthesis

Biological machine learning was long bottlenecked by our ability to synthesize designed DNA. Variational synthesis models control chemical reactions to physically manufacture quadrillions of designed sequences in DNA. However, training these variational synthesis models is challenging: constraints on chemical synthesis can force many parameters into a discrete space, limiting the ability to pre-train and fine-tune. In this article we train "free" variational synthesis models using stochastic gradient descent in continuous space, and then discretize with post-training quantization. This enables variational synthesis models to satisfy stringent reward criteria, while still producing diverse designs, achieving a strictly dominating quality-diversity Pareto frontier. We demonstrate by training variational synthesis models of peptides, antibody CDRH3s, regulatory DNA elements, and enzymes. *In silico* performance is maintained *in vitro*.