3D-chiral (2.5) atom-based TOMOCOMD-CARDD descriptors: theory and QSAR applications to central chirality codification

Abstract

The history of the use of chiral descriptors in Quantitative structure–activity relationships (QSAR) studies is described, with a particular emphasis on several series of novel chirality descriptors that have been introduced in this field. Specifically, chiral topological indices that circumvent the inability of conventional (chiral insensitive) topological molecular descriptors are reviewed. These modified descriptors were applied to several well-know data sets in order to validate each one of them. 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 researches using 3D-QSAR approaches such as Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), Molecular Quantum Similarity Measures (MQSM), 3D-chiral (2.5) TOMOCOMD-CARDD descriptors, Topological Quantum Similarity Indices (TQSI), similarity matrixes, Comparative Molecular Moment Analysis (CoMMA), E-state, Mapping Property Distributions of Molecular Surfaces (MAP), EVA and so on. For that reason, it was selected for the shake of comparability by us. An extensive comparison between all these approaches was updated. In addition, to evaluate the effectiveness of this novel approach in drug design we have modelled the angiotensin-converting enzyme inhibitory activity of perindoprilate’s σ-stereoisomers combinatorial library as well as to codify information related to pharmacological property highly dependent on molecular symmetry of a set of seven pairs of chiral N-alkylated 3-(3-hydroxyphenyl)-piperidines that bind σ-receptors. The validation of this method was achieved by comparison with previous reports applied to the same data sets. The non-stochastic and stochastic 3D-chiral (2.5) bilinear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.