Coarse-grained molecular models, in which atoms are grouped together into effective interaction sites, allow for larger time and length scales to be accessed, and the reduced degrees of freedom compared to all-atom models lead to a smoother energy landscape. Numerous studies spanning more than four decades have investigated different ways of parameterizing coarse-grained models intended to predict or reproduce the secondary structure of biomolecules. Most coarse-grained models of proteins or foldamers are either 1) inherently biased towards specific targeted structures (such as Gō-type models and elastic network models), or 2) parameterized to chemically specific atomistic simulation and/or experimental data. In Gō-type coarse-grained protein models, nonbonded interactions are restricted to native contact pairs, artificially stabilizing secondary structure. Nonetheless, Gō-type structure-based models have contributed valuable insight into protein folding mechanisms. Chemically specific coarse-grained models are typically parameterized using systematic coarse-graining techniques including iterative Boltzmann inversion, forcematching, relative entropy methods, or more recently, by machine learning techniques. This work is intended to improve understanding of foldamer behavior at a more fundamental level through the use of generic coarse-grained models free of chemical specificity. A reduced force field parameter space allows for efficient parameter scanning and optimization when searching for foldamers with novel characteristics. Coarse-grained modeling also enables efficient investigation of overall forces driving oligomer secondary structure formation, such as stiffness of the backbone and sidechains, interaction strengths between backbone and sidechains, and size of the sidechains.