The therapeutic discovery process generates large volume multi-dimensional datasets which are difficult to effectively manage to facilitate scientific decision-making. In support of large molecule discovery, the current state of bioinformatics capabilities is not well suited to uniquely register, and in turn, intuitively reveal key compositional differences versus an array of drug performance attributes. Moreover, the ability to unambiguously identify, compare, and communicate molecular compositions is integral to the scientific process, increasing efficiency of downstream data analysis including development of AI/ML methods. Here, we report our approach to the structure-based registration of peptide and biological entities with the capability of atomic-level differentiation. We also reveal data reduction and data visualization approaches to allow for fluid data interactivity and analysis of multivariate datasets related to therapeutic peptide discovery campaigns.