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John Markopoulos

Publications and source records attributed to John Markopoulos.

4 recordsLinked to original sources

A novel QSPR model for predicting θ (lower critical solution temperature) in polymer solutions using molecular descriptors.

In this study, we present a new model that has been developed for the prediction of θ (lower critical solution temperature) using a database of 169 data points that include 12 polymers and 67 solvents. For the characterization of polymer and solvent molecules, a number of molecular descriptors (topological, physicochemical,steric and electronic) were examined. The best subset of descriptors was selected using the elimination selection-stepwise regression method. Multiple linear regression (MLR) served as the statistical tool to explore the potential correlation among the molecular descriptors and the experimental data. The prediction accuracy of the MLR model was tested using the leave-one-out cross validation procedure, validation through an external test set and the Y-randomization evaluation technique. The domain of applicability was finally determined to identify the reliable predictions.

Linear Models↗

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Anti-HIV Agents↗

A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes.

A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and predicting induction of apoptosis by 4-aryl-4H-chromenes. The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 43 recently discovered 4-aryl-4H-chromenes. Among the 61 different physicochemical, topological, and structural descriptors that were considered as inputs to the model, seven variables were selected using the elimination selection-stepwise regression method (ES-SWR). The physical meaning of each descriptor is discussed. The accuracy of the proposed MLR model is illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined.

Algorithms↗

Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques.

A linear quantitative-structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity. For the selection of the best variables the Elimination Selection-Stepwise Regression Method (ES-SWR) is utilized. The predictive ability of the model is evaluated against a set of 13 compounds. Based on the produced QSAR model and an analysis on the domain of its applicability, the effects of various structural modifications on biological activity are investigated. The study leads to a number of guanidine derivatives with significantly improved predicted activities.

Algorithms↗