In this chapter, the updated strategies of statistical modeling, structural representation, and required steps for model construction and validation are presented, in the light of their application for the analysis, prediction, and future design of drug candidates with aldose reductase (ALR2) inhibitory activity. ALR2 constitutes an important target for the treatment of long-term complications of diabetes mellitus, while it is also implicated in ischemic and other inflammatory pathologic conditions as well as to some particular types of cancer. ARI models cover a large range from traditional and Fujita-Ban and Hansch analyses, using essential physicochemical properties, to models engaging complex structural representation and multidimensional three-dimensional up to six-dimensional QSAR models. Most ARI models are chemical class specific and are used to suggest new molecules with ARI activity. Their interpretation contributes to build and confirm the interaction network in the domain of the catalytic site, and they may be considered complementary to molecular modeling and structure-based design. Attempts for general chemical family-independent models are also reported. Following the evolution in QSAR restrictions and guidelines, model validation and model applicability domain gain a growing role for the suggestion of new compounds with ARI activity. Difficulties associated with selectivity, mainly against aldehyde reductase (ALR1) and ADME properties, are also discussed.