Background: Volar plate osteosynthesis in distal radius fractures (DRF) is inherent to various complications. Finite element (FE) analysis could help to improve and compare current treatment approaches. Existing FE models do, however, lack adequate experimental validation. The goal of this study was to generate and validate specimen specific FE models of distal radius fracture (DRF) osteosynthesis in a semi-automatic fashion. The influence of local bone density and orientation on the validation results was conducted as a side study. Materials and Methods: Biomechanical in-vitro experiments and specimen specific FE analysis were conducted on 34 fresh frozen, cadaveric human specimen with artificially created extra articular DRFs and volar locking plate osteosynthesis. The experimental spring stiffness was measured in uniaxial compression tests and compared to the elastic response of the FE models. QCT scans of the prepared samples were used to create the finite element models in rigorous accordance to the experiments. Local bone material data was incorporated based on HR-pQCT scans of the intact specimen. Three types of models were generated, with (a) density and fabric based bone material, (b) density based bone material and (c) homogeneous bone material. The finite element model stiffness was corrected for the machine compliance and linear regression analysis was performed to quantify the goodness of the predictions. Results: All three types of FE models over-estimated the experimental stiffness but were significantly correlated to the experimental results (p<0.0001). The coefficient of determination was similar for types (a) and (b) (R2=0.79) but considerably lower for type (c) (R2=0.55). Section forces at the screw-plate interface of the implant were evaluated in the FE models of type (a) and showed good agreement with experimental observations of screw-plate interface failures. Conclusion: The elastic response of FE models with density based bone material was highly correlated with the experimental spring stiffness. The proposed semi-automatic model generation methodology paves the way for future parameter variation studies which enables the comparison of multiple treatment options.