Computer simulations of soft matter require a compromise to be made between the computational efficiency and the resolution of a model studied. Highly resolved models can give insights into the interactions between individual atoms in a soft material. But, since these atomistic models are computational expensive, they are limited to small length scales and short time scales, which makes it difficult to compare simulation results with the ones from experiments in the laboratory. On the other hand, continuum models enable the study of soft matter at larger length and longer time scales and are computationally less expensive, but they rather focus on macroscopic properties than on their atomistic origin. A possible way to bridge the gap between these scales is to perform simulations at an intermediate level of resolution. The problem, which exists at this mesoscopic scale, is the lack of accurate models. Hence, new ones have to be built. The process to construct mesoscopic models based on information from the atomistic scale is commonly referred to as bottom-up coarse graining. Bottom-up coarse graining describes the process of lowering the resolution of a atomistic model to make it applicable at larger length and time scales. The major goal of this Ph.D. thesis is to increase the knowledge on so called structure-based bottom-up coarse graining techniques.These methods enable the derivation of coarse grained (CG) models, which accurately reproduce the structure of an atomistic or fine grained (FG) model at the mesoscopic scale. The shortcomings of different structure-based methods are carefully analyzed and new approaches to overcome them are presented.