There are three groups of methods for correlating and predicting LCSTs. The first group proposes models that are based on a solid theoretical background using liquid–liquid or vapor–liquid experimental data. These methods require experimental data to adjust the unknown parameters, resulting in limited predictive ability . Another approach uses empirical equations that correlate θ (LCST) with physicochemical properties such as density, critical properties etc., but suffers from the disadvantage that these properties are not always available. A new approach proposed by Liu and Zhong develops linear models for the prediction of θ(LCST) using molecular connectivity indices, which depends only on the solvent and polymer structures. The latter approach has proven to be a very useful technique in quantitative structure–activity/property relationships (QSAR/QSPR) research for polymers and polymer solutions. QSAR/QSPR studies constitute an attempt to reduce the trial-and-error element in the design of compounds with desired activity/properties by establishing mathematical relationships between the activity/property of interest and measurable or computable parameters, such as topological, physicochemical, stereochemistry, or electronic indices. More recently QSPR models for the prediction of the θ (LCST) using molecular (electronic, physicochemical etc.) descriptors have been published. Using validated robust QSPR models, experimental time and effort can be reduced significantly as reliable estimates of θ (LCST) for polymer solutions can be obtained before they are actually synthesized in the laboratory.