Continuous variational synthesis enables diverse designed dna production
Continuous Variational Synthesis
Machine Learning
Summary
Creating large numbers of specifically designed DNA sequences was limited by how well machines could make them. The authors developed a method where they train models in a smooth, continuous way to design DNA, then convert those designs into forms that meet physical manufacturing limits. This approach helps make diverse and high-quality DNA sequences for enzymes, peptides, antibodies, and regulatory parts. The designs worked well both on computer simulations and in lab experiments.
What this means in practice
- •For biotech developers: Create diverse DNA molecules like enzymes and antibodies that meet strict quality and manufacturing constraints using continuous training and quantization methods.
- •For synthetic biology engineers: Produce high-quality regulatory DNA elements for gene control by training models that translate designs into manufacturable sequences.
Authors
Alan N. Amin, Mattia G. Gollub, Andrei Slabodkin, Elizabeth B. Wood, Eli N. Weinstein
Abstract
Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions to physically manufacture quadrillions of designed sequences in DNA. However, training these generative 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 to impose hardware and wetware constraints. This enables variational synthesis models to satisfy stringent reward criteria, while still synthesizing diverse designs, achieving a strictly dominating quality-diversity Pareto frontier. We demonstrate by training variational synthesis models of enzymes, peptides, antibody CDRH3s, and regulatory DNA elements. In silico performance is maintained in vitro.