QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses

Abstract

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the Setschenow constants (K(salt)) by sodium chloride of organic compounds. The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability. The linear model fits the training set with R(2) = 0.8680, while ANN and SVM higher values of R(2) = 0.8898 and 0.9302, respectively. The validation results through the test set indicate that the proposed models are robust and satisfactory. The QSPR study suggests that the molecular lipophilicity is closely related to the Setschenow constants.

Prediction of Monomer Reactivity in Radical Copolymerizations from Transition State Quantum Chemical Descriptors

“…In SVM, systems use the input data into a high dimensional feature space and subsequently carry out the linear regression in the feature space. For a given data set (x 1 , y 1 ), (x 2 , y 2) , …, (x l , y l ), where x i ∈ R n , y i ∈ R (i = 1, 2,…, l), the linear critical function of support vector regression (SVR) is listed as below [10,18,19,22] :…”

“…Therefore, the development of reliable quantitative structure-activity relationship (QSAR) models for the prediction of the basic parameters u and v is of real interest, particularly for new monomers for which experimental investigation would be expensive. QSAR approaches can conserve resources and accelerate the process of development of new molecules [7][8][9][10][11] . Yi et al developed QSAR models for parameters u and v with quantum chemical descriptors calculated from radicals C 1 H 3 -C 2 HR 3 •.…”

Centroid origin shift of quantum object sets and molecular point clouds description and element comparisons

“…In order to distinguish QSPR procedures based upon quantum similarity from the empirical QSPR parametric framework, the terms: quantum QSPR and classical QSPR will be respectively employed. 2 Contemporary examples of the dimensionality paradox can be evidenced in some relatively recent papers chosen among quite a large quantity of publications like: (a) [8], where more than 700 molecules have been studied within a classical QSPR model set up, resulting in an equation bearing less than 10 descriptor parameters and (b) [9] where a set of 101 molecules was studied with QSPR 4 descriptor models. 3 Often in the QSPR or QSAR common literature the term chemical space appears.…”

Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network

“…That is why theoretical methods for calculation of quantitative structure-property/activity relationships (QSPR/QSAR) are widely used to study these interactions [8][9][10][11][12][13][14]. The QSPR/QSAR approach is based on the assumption that the variation of the behavior of the compounds, as expressed by any measured physicochemical properties, can be correlated with numerical changes in structural features of all compounds [15][16][17][18][19]. The advantage of this approach lies in the fact that it requires only the knowledge of the chemical structure and is not dependent on any experimental properties.…”