ABSTRACT
The development of models for the prediction of physicochemical or biological parameters through the application of classification or correlation modeling techniques has acquired utmost importance during the past few decades. The use of graph invariants in (quantitative) structure-activity/property relationship [(Q)SAR/QSPR] has served as a valuable source for nesting mechanistic information from chemical structure and property relationship studies. Such in silico intervention in the initial stages of the drug discovery process is now routinely used as a tool to prioritize experiments with an aim to improve the drug attrition rate. The timely prediction of this failure will naturally save considerable cost, valuable time, human efforts and minimize animal sacrifice. The availability of diverse types of topological indices (TIs) imparts meaningful information about the physicochemical framework would play a certain role in understanding the relationships between chemical structures and experimental outcomes. Various super-augmented eccentric connectivity indices, as well as their applications, have been briefly reviewed in the present chapter.
