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Workshop: Machine Learning for Engineering Modeling, Simulation and Design

Predicting Nanorobot Shapes via Generative Models

Emma Benjaminson · Rebecca Taylor · Matthew Travers


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, used to sense (e.g., changes in pH), or to store and release loads. 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, detecting an adverse reaction and then releasing 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 exactly characterize the shape and size of individual structures, is extremely time-consuming to collect, whereas low-fidelity data is readily available but only captures overall statistics for different processes. Therefore, this work combines low- and high-fidelity data to train a generative model using a two-step process. First, we pretrain the model using a relatively small (1000s), high-fidelity dataset to represent the distribution of nanorobot shapes. Second, we bias the learned distribution towards samples with certain physical properties that are measured using 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.

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