QSPR study of partition coefficient (Ko/w) of some organic compounds using radial basic function-partial least square (RBF-PLS)

Abstract

Neste trabalho, nós introduzimos um novo método da função de base radial por regressão de mínimos quadrados (RBF-PLS) com elevada exatidão e precisão nos estudos quantitativos da relação entre a estrutura-propriedade de compostos orgânicos (QSPR). Três métodos QSPR foram comparados para a predição dos coeficientes de partição no sistema n-octanol-água (K o/w ) (de alguns compostos orgânicos). A regressão linear múltipla (MLR), a regressão parcial dos mínimos quadrados (PLS) e a regressão base radial com funções pelo método de mínimos quadrados (RBF-PLS) foram empregadas para construir os modelos lineares e não-lineares e predizer o valor de K o/w . Os descritores teóricos foram calculados por Dragon e por Gaussian 98 e foram explorados pelas regressões parciais, codificando diferentes aspectos topológicos, geométricos e eletrônicos das estruturas moleculares. A raiz quadrada dos erros médios previstos (RMSEP) para as etapas de testes e da previsão teórica por modelos de MLR, de PLS e de RBF-PLS foram 0,4022; 0,4128; 0,3050; 0,3564; 0,0364 e 0,0533 respectivamente. Também, o erro padrão relativo previsto (RSEP) para os testes e de previsão teórica por MLR, PLS e RBF-PLS foram de 13,24; 13,60; 10,04; 11,74; 1,197 e 1,757 respectivamente. Os dados mostram que o RBF-PLS produziu resultados melhores do que PLS e MLR.In this work, we introduce a new method ability radial basic function-partial least square (RBF-PLS) with high accuracy and precision in QSPR studies. Three quantitative structure-propertty relationship (QSPR) methods have been compared for the prediction of n-octanol-water partition coefficients (K o/w ) of some organic compounds. The multiple linear regressions (MLR), partial least square (PLS) and radial basis function-partial least squares (RBF-PLS) models were employed to construct linear and nonlinear models to predict of K o/w . The theoretical descriptors that calculated by Dragon and Gaussian 98 were explored by stepwise regressions, encoding different aspects of the topological, geometrical and electronic molecular structures. The root means square error of prediction (RMSEP) for training and prediction sets by MLR, PLS and RBF-PLS models were 0.4022, 0.4128, 0.3050, 0.3564, 0.0364 and 0.0533, respectively. Also, the relative standard error of prediction (RSEP) for training and prediction sets by MLR, PLS and RBF-PLS models were 13. 24, 13.60, 10.04, 11.74, 1.197 and 1.757 respectively. The resultant data explained that RBF-PLS produced better results than PLS and MLR.

Experimental and Theoretical Study on Lipophilicity of Synthetic 1,2-Dithiole-3-thiones

“…Finally, we found fC=N = -1.092, in good agreement with the value fC=N = -1.064 30 . However, it is necessary to make additional research in order to study an exact value of this fragment.…”

“…For the validation of correlation between the values log Pexp and log Pcal, we use partial least squares (PLS) model. The statistical parameters 30 used to assess the quality of the model is the prediction error sum of squares (PRESS) of validation and finally the standard correlation coefficients R 2 .…”

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

“…This results in a significant improvement in prediction quality. Two years later, Goudarzi and Goodarzi [94] conducted a prediction of the n-octanol-water partition coefficient for the same dataset of organic compounds but using different techniques, namely, MLR, PLS, and RBF-PLS (radial basic function-partial least squares). This time, due to flexible mapping of the selected features by manipulating their functional dependence implicitly unlike regression analysis, RBF-PLS is considered to be better than MLR and PLS models.…”

Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model

“…The idea behind this method is to divide the value of a property of the complete molecule into its contributions based on the chemical groups or other molecular subunit. Group contribution models have been successfully applied to a wide variety of properties including density [1, 2], critical properties [3–5], enthalpy of vaporization [6], normal boiling points [7, 8], water–octanol partition coefficients [9–11], infinite dilution activity coefficients [12] and many more. Also, from Gibbs excess energy models [13–15] and equations of states [16–19] they provide an approach that allows widening their application range to molecules composed of the same chemical groups relatively easily.…”