Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate the development and utilization of computational approaches for biopolymer study and nanotechnology applications. Biopolymer nanotechnology exploits the natural chemistry of biopolymers to perform novel functions at the nanoscale. Molecular dynamics is the numerical simulation of chemical entities according to the physical laws of motion and statistical mechanics. The number of atoms in biopolymers require coarse-grained methods to fully sample the dynamics of the system with reasonable resources. Accordingly, a coarse-grained molecular dynamics model for the characterization of hybrid nucleic acid-protein nanotechnology was developed. Proteins are represented as an anisotropic network model (ANM) which show good agreement with experimentally derived protein dynamics for a small computational cost. The model was subsequently applied to hybrid DNA-protein cages systems and exhibited excellent agreement with experimental results. Ongoing development efforts look to apply network models to oxDNA origami to create multiscale models for DNA origami. The network approximation will allow for detailed simulation of DNA origami association, of concern to DNA crystal and lattice formation. Identification and design of target-specific binders (aptamers) has received considerable attention on account of their diagnostic and therapeutic potential. Generated in selection cycles from extensive random libraries, biopolymer aptamers are of particular interest due to their potential non-immunogenic properties. Machine learning leverages the use of powerful statistical principles to train a model to transform an input into a desired output. Parameters of the model are iteratively adjusted according to the gradient of the cost function. An unsupervised and generative machine learning model was applied to Thrombin aptamer sequence data. From the model, sequence characteristics necessary for binding were identified and new aptamers capable of binding Thrombin were sampled and verified experimentally. Future work on the development and utilization of an unsupervised and interpretable machine learning model for unaligned sequence data is also discussed.