Showing papers by "Georgia Melagraki published in 2006"

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

Abstract: Summary 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 = 0.8160; SPRESS = 0.5680) proved to be very accurate both in training and predictive stages.

Prediction of toxicity using a novel RBF neural network training methodology.

Abstract: A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity. The dataset used consists of 221 phenols and their corresponding toxicity values to Tetrahymena pyriformis. Physicochemical parameters and molecular descriptors are used to provide input information to the models. The performance and predictive abilities of the RBF models are compared to standard multiple linear regression (MLR) models. The leave-one-out cross validation procedure and validation through an external test set produce statistically significant R2 and RMS values for the RBF models, which prove considerably more accurate than the MLR models. [Figure: see text].

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

Abstract: Summary 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 SelectionStepwise 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.

A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri.

Abstract: This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischeri for a heterogeneous set of compounds.

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

Abstract: 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.

A Novel QSAR Model for Evaluating and Predicting the Inhibition Activity of Dipeptidyl Aspartyl Fluoromethylketones

Abstract: A linear quantitative structure activity relationship model is obtained using Multiple Linear Regression (MLR) analysis as applied to a series of 49 dipeptidyl aspartyl fluoromethylketone derivatives with inhibitory activity of the caspase enzyme. For the selection of the best descriptors, the elimination selection stepwise regression method is utilized. 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.

A simple synthesis of functionalized 2-amino-3-cyano-4-chromones by application of the N-hydroxybenzotriazole methodology

Abstract: A novel and simple method for the synthesis of functionalized 2-amino-3-cyano-4-chromones is reported. The title compounds were isolated after acylation of malononitrile with Nhydroxybenzotriazolyl acetylsalicylates, generated in situ by treating acetylsalicylic acid derivatives with N-hydroxybenzotriazole, followed by cyclization. The described one-pot methodology is characterized by short reaction times, high yields (68 to 77%), no side-products and provides chromones with a variety of substituents on the aromatic ring. The structure of the isolated compounds has been determined by means of H/C NMR and FT-IR Spectroscopy.