  The field of DNA nanotechnology has made it possible to assemble, with high
yields, different structures that have actionable properties. For example,
researchers have created components that can be actuated. An exciting next step
is to combine these components into multifunctional nanorobots that could,
potentially, perform complex tasks like swimming to a target location in the
human body, detect an adverse reaction and then release a drug load to stop it.
However, as we start to assemble more complex nanorobots, the yield of the
desired nanorobot begins to decrease as the number of possible component
combinations increases. Therefore, the ultimate goal of this work is to develop
a predictive model to maximize yield. However, training predictive models
typically requires a large dataset. For the nanorobots we are interested in
assembling, this will be difficult to collect. This is because high-fidelity
data, which allows us to characterize the shape and size of individual
structures, is very time-consuming to collect, whereas low-fidelity data is
readily available but only captures bulk statistics for different processes.
Therefore, this work combines low- and high-fidelity data to train a generative
model using a two-step process. We first use a relatively small, high-fidelity
dataset to train a generative model. At run time, the model takes low-fidelity
data and uses it to approximate the high-fidelity content. We do this by
biasing the model towards samples with specific properties as measured by
low-fidelity data. In this work we bias our distribution towards a desired node
degree of a graphical model that we take as a surrogate representation of the
nanorobots that this work will ultimately focus on. We have not yet accumulated
a high-fidelity dataset of nanorobots, so we leverage the MolGAN architecture
[1] and the QM9 small molecule dataset [2-3] to demonstrate our approach.
