Nowadays, variant strategies are proposed and evaluated to find the best scenario for upgrading the high-accurate QSAR/QSPR modeling, particularly on nano-scale. One of the most interesting samples is nanofluids because of high potential in heat transfer applications. In the case of nano-QSPR, some optimum empirical conditions and characteristic features (e.g., size of nanoparticles and temperature) play impressive roles in nanofluids’ properties. Quasi-simplified molecular inputline entry-system (quasi-SMILES) is nominated as valuable linear notation to meet the demands for representation of nanofluids, either chemical structure or defined conditions. The outcomes of nano-QSPR modeling of nanofluids by quasi-SMILES not only make possible the incorporation of molecular structure with experimental conditions in modeling process but also reveal the influence of some molecular features on studied thermophysical properties. Herein, recent studies on the development of predictive models of nanofluids using quasi-SMILES, which is a new trend to estimate the properties of nanofluids, were discussed comprehensively. It is remarkable to point out that the statistical evaluation of proposed models confirmed the predictability power, reliability, and credit of developed models in all reported cases. It is rational that scholars are working on improving QSAR/QSPR modeling; employing quasi-SMILES is an open opportunity to overcome the limitations of conventional molecular representation.
