Using machine learning to design adeno-associated virus capsids with high likelihood of viral assembly

We study the application of Machine Learning in designing AAV2 capsid sequences with high likelihood of viral assembly, i.e. capsid viability. Specifically, we design and implement Origami, a model-based optimization algorithm, to identify highly viable capsid sequences within the vast space of 2033 possibilities. Our evaluation shows that Origami performs well in terms of optimality and diversity of model-designed sequences. Moreover, these sequences are ranked according to their viability score. This helps designing experiments given budget constraint.