title: 3D-chiral atom, atom-type, and total non-stochastic and stochastic molecular linear indices and their applications to central chirality codification.
abstract: Non-stochastic and stochastic 2D linear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These descriptors circumvent the inability of conventional 2D non-stochastic [Y. Marrero-Ponce. J. Chem. Inf. Comp., Sci. l 44 (2004) 2010] and stochastic [Y. Marrero-Ponce, et al. Bioorg. Med. Chem., 13 (2005) 1293] linear indices to distinguish sigma-stereoisomers. In order to test the potential of this novel approach in drug design we have modelled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models showed an accuracy of 100% and 96.65% for the training set; and 88.88% and 100% in the external test set, respectively. Canonical regression analysis corroborated the statistical quality of these models (R(can) of 0.78 and of 0.77) and was also used to compute biology activity canonical scores for each compound. After that, the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by linear multiple regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R2 = 0.982 and s = 0.157) and stochastic (R2 = 0.941 and s = 0.267) 3D-chiral linear indices were used. The predictive power was assessed by the leave-one-out cross-validation experiment, yielding values of q2 = 0.982 (s(cv) = 0.186) and q2 = 0.90 (s(cv) = 0.319), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The best results obtained in the cross-validation procedure with non-stochastic (q2 = 0.904) and stochastic (q2 = 0.88) 3D-chiral linear indices are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide an interesting alternative to other more common 3D-QSAR descriptors.
authors.affiliation: Department of Pharmacy, Faculty of Chemical-Pharmacy, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba. yovanimp@qf.uclv.edu.cu
authors.forename: Yovani
authors.initials: Y
authors.lastname: Marrero-Ponce
authors.forename: Juan A
authors.initials: JA
authors.lastname: Castillo-Garit
mesh_terms: D000806:Angiotensin-Converting Enzyme Inhibitors / Q000737:chemistry*; D015195:Drug Design; D008958:Models, Molecular; D010880:Piperidines / Q000737:chemistry / Q000378:metabolism; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D017480:Receptors, sigma / Q000819:agonists* / Q000737:chemistry; D013237:Stereoisomerism; D013256:Steroids / Q000737:chemistry / Q000378:metabolism; D013269:Stochastic Processes
chemical_list: D000806:Angiotensin-Converting Enzyme Inhibitors; D010880:Piperidines; D017480:Receptors, sigma; D013256:Steroids