Utilizing nanofluids as a suspension containing nanoparticles in an ordinary liquid is a relatively new field, which received great attention in recent years. The main object of this investigation is focused on modeling the effective viscosity of nanofluids using the nano-quantitative structure-property relationship (nano-QSPR) paradigm. Two distinct data sets were considered containing four types of nanoparticles (Al2O3, CuO, SiO2, and ZnO) dispersed in water (as the most common base fluid) at volume fraction ranges of 1–5% and various shapes (blades, bricks, cylindrical, spherical, and platelets). Simplified Molecular Input-Line Entry System (SMILES) is a tool to represent the molecular structure. Quasi-SMILES is a sequence of symbols that represents all available data e.g. molecular structure together with physicochemical conditions. Taking into account the capability of quasi-SMILES molecular representation to define the eclectic data such as size and shape of nanoparticles, this notation was chosen to exemplify nanofluids structure. It is remarkable to point out that the proposed attitude to generate nano-QSPR models introduced a comparison between two specific predictive potential criteria using by Monte Carlo technique. It was concluded that the development of models based on Correlation intensity index (CII) is statistically more reliable than model generation based on the Index of ideality of correlation (IIC).
