Quantitative structure-solubility relationships (QSSR) are considered as a type of Quantitative structure-property relationship (QSPR) study in which aqueous solubility of chemicals are related to chemical structure. In the present work, multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for QSSR studies of the water solubility of 68 phenols (phenol and its derivatives) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and a genetic algorithm (GA), the descriptors that resulted in the best fitted models were selected. After descriptor selection, multiple linear regression (MLR) was used to construct a linear QSSR model. The R 2 = 91.0 %, LOO 2 Q = 89.33 %, s = 0.340 values of the model developed by MLR showed a good predictive capability for log S values of phenol and its derivatives. The results of MLR model were compared with those of the ANN model. the comparison showed that the R 2 = 94.99 %, s = 0.245 of ANN were higher and lower, respectively, which illustrated an ANN presents an excellent alternative to develop a QSSR model for the log S values of phenols to MLR. age to the environment, plants, animals and human health. The compounds penetrate ecosystems as the result of drainage of municipal or industrial sewage to surface water. 4 Therefore, it is vital to protect the environment and prevent their behavior by studying their physicochemical properties.Aqueous solubility is the concentration of a chemical in the aqueous phase, when the solution is in equilibrium with the pure compound in its usual phase (gas, liquid or solid) under standard conditions of temperature and pressure. 5 Aqueous solubility is one of the major physiochemical properties to be optimized in pharmaceutical and environmental studies; it is related to absorption and distribution in ADME-Tox (absorption, distribution, metabolism, excretion-toxicity).Experimental determination of compound solubility is not easily managed, or even possible, when working with large chemical libraries. 6 Alternately, the quantitative structure-property relationship (QSPR) provides a promising method for the prediction of solubility using descriptors derived solely from the molecular structure to fit experimental data. The QSPR approach attempts to establish simple mathematical relationships to describe the correlation of a given property to molecular structures for a set of compounds. 7 The advantage of this method lies in the fact that it requires only knowledge of the chemical structure and is not dependent on any experimental property. Moreover, it could be used for the prediction of the properties of new compounds.Recently, several works reported QSPR studies on the solubility and other properties. Warne et al. 8 reported the utility of thirty-nine molecular descriptors and physicochemical properties to model the solubility (S) and octanol-water partition coefficient (K ow ) of thirty-one lipophilic or...
Indolizine derivatives hold essential biological functions and have been researched for hypoglycemic, antibacterial, anti-inflammatory, analgesic, and anti-tumor actions. Indolizine scaffold has intrigued conjecture and continuous attention and has become an effective parent system for generating powerful novel medication candidates. This research focused on applying the quantitative structure-electrochemistry relationship (QSER) approach to the half-wave potential (E1/2) for Indolizine derivatives using theoretical molecular descriptors. After calculating the descriptors and splitting the data into both sets, training and prediction. The QSER model was constructed using the Genetic Algorithm/Multiple Linear Regression (GA/MLR) technique, which was used to choose the optimal descriptors for the model. A four-parameter model has been established. Many assessment procedures, including cross-validation, external validation, and Y-scrambling testing, were used to assess the model's performance. Furthermore, the applicability domain (AD) was investigated using the Williams and Insubria graphs to assess the correctness of the established model's predictions. The constructed model exhibits great goodness-of-fit to experimental data, as well as high stability (R²=0.893, Q²LOO= 0.851, Q²LMO=0.843 RMSEtr= 0.052, s= 0.056). Prediction results show a good agreement with the experimental data of E1/2 (R²ext= 0.912, Q²F1= 0.883, Q²F2= 0.883, Q²F3= 0.919, CCCext= 0.942, RMSEext=0.045).
This work aims to reveal the correlation of the boiling point values of phenolic compounds with their molecular structures using a quantitative structure-property relationship (QSPR) approach. A large number of molecular descriptors have been calculated from molecular structures by the DRAGON software. In this study, all 56 phenolic compounds were divided into two subsets: one for the model formation and the other for external validation, by using the Kennard and Stone algorithm. A four-descriptor model was constructed by applying a multiple linear regression based on the ordinary least squares regression method and genetic algorithm/variables subsets selection. The good of fit and predictive power of the proposed model were evaluated by different approaches, including single or multiple output cross-validations, the Y-scrambling test, and external validation through prediction set. Also, the applicability domain of the developed model was examined using Williams plot. The model shows R² = 0.876, Q² LOO = 0.841, Q² LMO = 0.831 and Q² EXT = 0.848. The results obtained demonstrate that the model is reliable with good predictive accuracy.
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