QSAR modeling using chirality descriptors derived from molecular topology

Topological descriptors of chemical structures (such as molecular connectivity indices) are widely used in Quantitative Structure-Activity Relationships (QSAR) studies. Unfortunately, these descriptors lack the ability to discriminate between stereoisomers, which limits their application in QSAR. To circumvent this problem, we recently introduced chirality descriptors derived from molecular graphs and applied them in QSAR studies of ecdysteroids (Golbraikh A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001,41, 147-158). In this paper, we extend our earlier work by applying chirality descriptors to four data sets containing chiral compounds. All models were derived with the k-nearest neighbors (kNN) QSAR method developed in our laboratory (Zheng, W.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2000, 40, 185-194). They were validated using the same training and test sets that were employed in various, mostly 3D-QSAR, investigations published by other authors. We show that for all data sets 2D-QSAR models that use a combination of chirality descriptors with conventional (chirality insensitive) topological descriptors afford better or similar predictive ability as compared to models generated with 3D-QSAR approaches. The results presented in this paper reassure that 2D-QSAR modeling provides a powerful alternative to 3D-QSAR.