![Sample our Computer Science journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5928/TOC-Subject-Sample-2021-Computer-Science.jpg"})
Abstract
An efficient algorithm for deriving QSPR/QSAR models with nonorthogonal and ordered orthogonal descriptors, based on orthogonalization of topological indices, is presented. It is applied to structure-boiling point modeling of nonanes as the test case. The selection of the best descriptors from multivariate linear regression modeling is carried out using descriptors which are first orthogonalized. It is shown that such an algorithm is applicable for the selection of the best descriptors in a multivariate linear regression model even to very large sets of descriptors. A computationally-effective method for the (ordered) orthogonalization of topological indices is also introduced. By the use of an ordered orthogonalization procedure it is possible to select the best order of descriptors for orthogonalization. The orthogonalization in the selected order of descriptors produces models with a smaller number of significant descriptors. The comparison between QSPR/QSAR models with nonorthogonal and ordered orthogonal topological indices favors the latter models. It is also shown that the enlarging of the basis set results in better QSPR/QSAR models, but it is not possible to know in advance whether it is better to use a nonlinear transformation of the smaller basis or to compute additional descriptors.
