Machine learning for biological sequence design with therapeutic applications

Experimental breakthroughs allow data on the relationship between sequence and function to be rapidly acquired. This data can be used to train and validate machine learning models that predict protein function directly from sequence. However, the cost and latency of wet-lab experiments require methods that find good sequences in few experimental rounds, though each round can contain large batches of sequence designs. In this setting, I will discuss model-based optimization approaches that allow us to take advantage of sample inefficient methods and find diverse optimal sequence candidates for experimental evaluation. The potential of this approach is illustrated through the design and experimental validation of viable AAV capsid protein variants for gene therapy applications in addition to the design and validation of peptides as potential therapeutics.