The central problem in QSPR (Quantitative Structure-Property Relationship) / QSAR (Quantitative Structure-Activity Relationship) is to convert chemical structures into molecular descriptors that are relevant to a certain physico-chemical property or biological activity. Although various physico-chemical properties may be subjected to QSPR/QSAR studies, the major application field of these models is drug discovery. Classically, drug discovery mainly relied on chance and massive screening of large libraries of synthesized or natural compounds. By contrast, computer-aided drug design, CADD, is an approach to rational drug design made possible by the recent advances in computational chemistry (in its various fields, such as molecular graphics, quantum chemistry, molecular mechanics, molecular dynamics, library searching, prediction of physico-chemical and biological properties) and, of course, in computer science.
Many physico-chemical properties can be satisfactorily correlated with the topostructural or topochemical features. Topological indices are among the simplest and efficient descriptors for QSPR/QSAR. In principle, these are mathematical objects, without a precise physical meaning. However, they may express the topological shape or may correlate with the molecular volume or surface area, as is shown in the pages of this book.
