Rodent models of schizophrenia (SCZ) are indispensable when screening for novel treatments, but quantifying their translational relevance with the underlying human pathophysiology has proved difficult. A novel systems methodology (shown in Figure 1) was developed integrating and comparing proteomic data of anterior prefrontal cortex tissue from SCZ post-mortem brains and matched controls with data obtained from four established glutamatergic rodent models, with the aim of evaluating which of these models represent SCZ most closely. Liquid chromatography coupled tandem mass spectrometry (LC-MSE) proteomic profiling was applied comparing healthy and “disease state” in human post-mortem samples and rodent brain tissue samples. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of pathophysiological characteristics of the disorder, which were represented across all four rodent models. Subsequently, these functional domains were used for cross-species comparisons. Five functional domains such as “development and differentiation” represented across all four rodent models, were identified. It was quantified that the chronic phencyclidine (cPCP) model represented SCZ brain changes most closely for four of these functional domains, by using machine-learning techniques. This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of SCZ most closely. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating behavioural endophenotypes with distinct biological processes.