This work presents the development of molecular-based mathematical models for the prediction of electrical conductivity of deep eutectic solvents (DESs). Two new quantitative structure-property relationship (QSPR) models based on conductor-like screening model for real solvent (COSMO-RS) molecular charge density distributions (S-sigma-profiles) were developed using the data obtained from the literature. The data comprise 236 experimental electrical conductivity measurements for 21 ammonium- and phosphonium-based DESs, covering a wide range of temperatures and molar ratios. First, the hydrogen-bond acceptors (HBAs) and hydrogen-bond donors (HBDs) of each DES were successfully modeled using COSMO-RS. Then, the calculated S-sigma-profiles were used as molecular descriptors. The relation between the conductivity and the descriptors in both models has been expressed via multiple linear regression. The first model accounted for the structure of the HBA, the HBD, the molar ratio, and temperature, whereas the second model additionally incorporated the interactions between the molecular descriptors. The results showed that by accounting for the interactions, the regression coefficient (R-2) of the predictive model can be increased from 0.801 to 0.985. Additionally, the scope and reliability of the models were further assessed using the applicability domain analysis. The findings showed that QSPR models based on S-sigma-profiles as molecular descriptors are excellent at describing the properties of DESs. Accordingly, the obtained model in this work can be used as a useful guideline in selecting DESs with the desired electrical conductivity for industrial applications.