The molecular representation of hydrocarbon mixtures is critical to the advanced kinetics modeling of refining conversion processes; however, the achievement of such a representation is considered a significant challenge. The isomeric lump in a homologous series sets the analytical limit in analytical characterization of middle and heavy distillates. This paper proposes a new procedure for de-lumping detailed analytical information generated using a gas chromatography-field-ionization mass spectrometry (GC-FIMS) method into a molecular representation. As a result, concentration distributions of the various molecules in the sample of interest are calculated. This paper presents a deterministic computer-assisted procedure that automatically (i) generates hydrocarbon molecules according to literature-based set of rules, (ii) selects the hydrocarbon molecules that are most thermodynamically stable (likely to exist), (iii) optimizes the three-dimensional geometry of those molecules and calculates their thermodynamic properties, and (iv) calculates the concentration distributions of those molecules. A separate validation calculation involves (i) prediction of the physical properties for pure hydrocarbons using quantitative structure-property relationship (QSPR) correlations; and (ii) prediction of bulk physical properties of this mixture using the calculated concentrations, properties of its pure hydrocarbon components, and suitable mixing rules. This procedure was applied to find molecular representations of five middle-distillate samples and the results were validated through the estimation and comparison of such simulated and measured physical properties as density, refractive index, and simulated distillation curves (the latter of which are used as a consistency check). Good agreement was observed between the predicted and measured properties for all five middle-distillate samples. This agreement validates the molecular characterization algorithm, at least for the purpose of bulk property prediction in middle distillates.