title: Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification.
abstract: Non-stochastic and stochastic 2D bilinear indices have been 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 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 had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R(2)=0.953 and s=0.238) and stochastic (R(2)=0.961 and s=0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q(2)=0.935 (s(cv)=0.259) and q(2)=0.946 (s(cv)=0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results 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 a very interesting alternative to other more common 3D-QSAR descriptors.
authors.affiliation: Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba. juancg@uclv.edu.cu
authors.forename: Juan A
authors.initials: JA
authors.lastname: Castillo-Garit
authors.forename: Yovani
authors.initials: Y
authors.lastname: Marrero-Ponce
authors.forename: Francisco
authors.initials: F
authors.lastname: Torrens
authors.forename: Richard
authors.initials: R
authors.lastname: Rotondo
mesh_terms: D000806:Angiotensin-Converting Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D003198:Computer Simulation; D015195:Drug Design; D066298:In Vitro Techniques; D007211:Indoles / Q000737:chemistry / Q000494:pharmacology; D016014:Linear Models; D008958:Models, Molecular; D010880:Piperidines / Q000737:chemistry / Q000494:pharmacology; D021281:Quantitative Structure-Activity Relationship; D017480:Receptors, sigma / Q000037:antagonists & inhibitors; D055672:Static Electricity; D013237:Stereoisomerism; D013256:Steroids / Q000737:chemistry / Q000494:pharmacology; D013269:Stochastic Processes; D014156:Transcortin / Q000187:drug effects
chemical_list: D000806:Angiotensin-Converting Enzyme Inhibitors; D007211:Indoles; D010880:Piperidines; D017480:Receptors, sigma; D013256:Steroids; C053500:perindoprilat; D014156:Transcortin