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Pogliani L.,University of Valencia | Pogliani L.,MOLware SL | De Julian-Ortiz J.V.,University of Valencia | De Julian-Ortiz J.V.,MOLware SL | Besalu E.,University of Girona
Match | Year: 2014

The Titius-Bode relationship, together with a qualitative example from the Ptolemaic cosmological theory, are used to introduce a brief discussion about, not only the validity of relationships, but also about the predictive character, usefulness, and meaning of QSPR/QSAR studies. Source

Pogliani L.,University of Valencia | Pogliani L.,MOLware SL | De Julian-Ortiz J.V.,University of Valencia | De Julian-Ortiz J.V.,MOLware SL
RSC Advances | Year: 2013

Eleven properties have been modeled with the objective of checking the importance for model purposes of mixed descriptors made of empirical parameters, molecular connectivity indices and random numbers. The mixed descriptors with random indices have a descriptive character which is satisfactorily confirmed by the leave-one-out method of statistical analysis. The introduction of a partition of the set of compounds into training and evaluation sets decreases drastically the probability to find a mixed descriptor with random indices with good model quality. Two properties, the magnetic susceptibility and the elutropic values, insist on having optimal descriptors with random indices. The overall model study underlines the importance of semiempirical descriptors made of experimental parameters and molecular connectivity indices, as well as the importance of a perturbation parameter that has been introduced into the valence delta number to encode the contribution of the depleted hydrogen atoms. The use of complete graphs to encode the core electrons of higher-row atoms is also underlined. The model quality of the mixed descriptors obtained with combinatorial regressive methods has also been tested with three-layered feed-forward artificial neural network (ANN) methods. This methodology not only confirms the validity of the descriptors but also improves their model quality widening, thus, their predictive ability. This journal is © The Royal Society of Chemistry 2013. Source

de Julian-Ortiz J.V.,MOLware SL | de Julian-Ortiz J.V.,University of Valencia | Besalu E.,University of Girona | Pogliani L.,MOLware SL | Pogliani L.,University of Valencia
Current Computer-Aided Drug Design | Year: 2014

Discerning between the concepts of difficulty and usefulness of a molecular ranking classification is of significant importance in virtual design chemistry. Here, both concepts are viewed from the statistical and practical point of view according to the standard definitions of enrichment and statistical significance p-values. These parameters are useful not only to compare distinct rankings obtained for the same molecular database, but also in order to compare the ones established in distinct molecular sets from an objective point of view. © 2014 Bentham Science Publishers. Source

Pogliani L.,MOLware SL | Pogliani L.,University of Valencia | Vicente De Julian-Ortiz J.,MOLware SL | Vicente De Julian-Ortiz J.,University of Valencia
RSC Advances | Year: 2014

New type of indices, the mean molecular connectivity indices (MMCI), based on nine different concepts of mean are proposed to model, together with molecular connectivity indices (MCI), experimental parameters and random variables, eleven properties of organic solvents. Two model methodologies are used to test the different descriptors: the multilinear least-squares (MLS) methodology and the Artificial Neural Network (ANN) methodology. The top three quantitative structure-property relationships (QSPR) for each property are chosen with the MLS method. The indices of these three QSPRs were used to train the ANNs that selected the best training sets of indices to estimate the evaluation sets of compounds. The best ANN relationships for most properties are of the semiempirical types that include mean molecular connectivity indices (MMCI), molecular connectivity indices (MCI) and experimental parameters. Refractive index, RI, viscosity, η, and surface tension, γ, prefer a semiempirical relationship made of MCI and an experimental parameter only. In our previous study with no MMCI, random variables contributed to semiempirical relationships for two properties at the ANN level (MS, and El), here the use of MMCIs undo the contribution of such variables. Most of the MMCIs that contribute to improve the model of the properties are valence-delta-dependent (δv), that is, they encode both the hydrogen atom contribution and the core electrons of higher-row atoms. © 2014 the Partner Organisations. Source

De Julian-Ortiz J.V.,University of Valencia | De Julian-Ortiz J.V.,MOLware SL | Zanni R.,University of Valencia | Galvez-Llompart M.,University of Valencia | Garcia-Domenech R.,University of Valencia
Current Drug Metabolism | Year: 2014

Human Intestinal Absorption (HIA) has been modeled many times by using classification models. However, regression models are scarce. Here, Artificial Neural Networks (ANNs) are implemented for this purpose. A dataset of structurally diverse chemicals with their respective experimental HIA were used to design robust, true predictive and widespread applicable ANN models. An input variables pool was made up of structural invariants calculated by using either Dragon or our software Desmol 1. The selection of best variables was performed following three steps using the entire dataset of molecules. Firstly, variables poorly correlated with the experimental data were eliminated. Secondly, input variable selection was performed by stepwise multilinear regression. Thirdly, correlation matrix in the set of selected variables was then obtained to eliminate those variables strongly intercorrelated. Backpropagation ANNs were trained for these variables finally selected as inputs, and HIA as output. The training and selection procedure to find robust models consisted of randomly partitioning the dataset into three sets: training set, with 50% of the population, test set with 25%, and validation set with the other 25%. With each partitioning, diverse numbers of hidden nodes were assayed to optimize the performance in the prediction for the three sets. Models with r2 greater than 0.6 for the three sets were considered as robust. A randomization test following all these steps was performed, and the poor results obtained confirm the validity of the method presented in this paper to predict HIA for datasets of structurally diverse organic compounds. © 2014 Bentham Science Publishers. Source

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