The concept of bond-based quadratic indices is generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design, we have modeled several well-known data sets. In particularly, Cramer’s steroid data set has become a benchmark for the assessment of novel QSAR methods. This data set has been used by several researchers using 3D-QSAR approaches. Therefore, it is selected by us for the shake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design, we model the angiotensin-converting enzyme inhibitory activity of perindoprilate’s s-stereoisomers combinatorial library, as well as codify information related to a pharmacological property, highly dependent on the molecular symmetry, of a set of seven pairs of chiral N-alkylated 3-(3-hydroxyphenyl)-piperidines, which bind s-receptors. The validation of this method is achieved by comparison with earlier publications applied to the same data sets. The non-stochastic and stochastic bond-based 3D-chiral quadratic indices appear to provide a rather interesting alternative to other more common 3D-QSAR descriptors.