Characterizing DNA Origami Nanostructures in TEM Images Using Convolutional Neural Networks

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that CNN models can characterize DNA origami nanostructures employed in programmable self-assembly, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models, namely, AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pretrain CNN models using a large image data set of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pretrained CNN models, using a small experimental TEM data set with 146 TEM images. All CNN models were found to have similar computational time requirements, although their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pretrained CNN models, ResNet50 and VGG16 have the highest and second-highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement with the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the number of ligation sites of nanostructures in large TEM images.