Home About us Contact
|
Home About us Contact | ||
|
|
||
Quantitative Structure-Property Relationship (quantitative + structure-property_relationship)
Selected AbstractsQSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random CopolymersMOLECULAR INFORMATICS, Issue 8-9 2010Carlo Giuseppe Bertinetto Abstract The glass transition temperature (Tg) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing predefined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275,copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9,K, a standard error of prediction (S) of 6.1,K and a squared correlation coefficient (R2) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting. [source] Prediction of Volatile Components Retention Time in Blackstrap Molasses by Least-Squares Support Vector MachineMOLECULAR INFORMATICS, Issue 5 2008Yongna Yuan Abstract House flies are pestiferous insects that have the potential to spread many diseases to humans and livestock, so it is very significant for us to manage house fly populations. Many commercial types of bait are available to attract house flies, but most are designed for outdoor or limited indoor use, due to their malodorous components. This study sought to identify compounds present in blackstrap molasses that might be attractive to house flies. An effective Quantitative Structure-Property Relationship (QSPR) model between the Retention Time (RT) and five molecular descriptors of the volatile compounds in blackstrap molasses, was built using a modified algorithm of Least-Squares Support Vector Machine (LS-SVM). Descriptors calculated from the molecular structures alone were used to represent the characteristics of compounds. The five molecular descriptors selected by the Heuristic Method (HM) in CODESSA were used as inputs for LS-SVM. The results obtained by LS-SVM were compared with those obtained by the HM. The LS-SVM model gives better results with the predicted correlation coefficient () 0.919 and Root Mean-Square Errors (RMSE) 2.193 for the test set, as well as that 0.824 and 2.728 in the MLR model. The prediction results of log RT are in very good agreement with the experimental values. This paper provided a new and effective method for predicting the chromatography retention index. [source] Quantitative structure property relationship models for the prediction of liquid heat capacityMOLECULAR INFORMATICS, Issue 1 2003Xiaojun Yao Abstract Quantitative Structure-Property Relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of liquid heat capacity at 25,°C using a diverse set of 871 organic compounds. The molecular descriptors used to represent molecular structures include constitutional and topological indices and quantum chemical parameters. Forward stepwise regression and radial basis function neural networks (RBFNNs) were used to construct the QSPR models. The root mean square errors in liquid heat capacity predictions for the training, test and overall data sets are 16.857, 18.744 and 17.141 heat capacity units, respectively. The prediction results are in agreement with the experimental values, but the RBFNN model seems to be better than stepwise regression method. [source] Notes on quantitative structure-properties relationships (QSPR) (1): A discussion on a QSPR dimensionality paradox (QSPR DP) and its quantum resolutionJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 7 2009Ramon Carbó-Dorca Abstract Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown properties of known molecular structures are obtained. However, the reduced descriptor dimension causes linear dependence within the set of discrete vector molecular representations, leading to positive semi-definite Gram matrices in molecular spaces. To resolve this QSPR dimensionality paradox (QSPR DP) here is proposed to adopt as starting point the quantum QSPR (QQSPR) computational framework perspective, where density functions act as infinite dimensional descriptors. The fundamental QQSPR equation, deduced from employing quantum expectation value numerical evaluation, can be approximately solved in order to obtain models exempt of the QSPR DP. The substitution of the quantum similarity matrix by an empirical Gram matrix in molecular spaces, build up with the original non manipulated discrete molecular descriptor vectors, permits to obtain classical QSPR models with the same characteristics as in QQSPR, that is: possessing a certain degree of causality and explicitly independent of the descriptor dimension. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009 [source] Nonlinear quantitative structure-property relationship modeling of skin permeation coefficientJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 11 2009Brian J. Neely Abstract The permeation coefficient characterizes the ability of a chemical to penetrate the dermis, and the current study describes our efforts to develop structure-based models for the permeation coefficient. Specifically, we have integrated nonlinear, quantitative structure-property relationship (QSPR) models, genetic algorithms (GAs), and neural networks to develop a reliable model. Case studies were conducted to investigate the effects of structural attributes on permeation using a carefully characterized database. Upon careful evaluation, a permeation coefficient data set consisting of 333 data points for 258 molecules was identified, and these data were added to our extensive thermophysical database. Of these data, permeation values for 160 molecular structures were deemed suitable for our modeling efforts. We employed established descriptors and constructed new descriptors to aid the development of a reliable QSPR model for the permeation coefficient. Overall, our new nonlinear QSPR model had an absolute-average percentage deviation, root-mean-square error, and correlation coefficient of 8.0%, 0.34, and 0.93, respectively. Cause-and-effect analysis of the structural descriptors obtained in this study indicates that that three size/shape and two polarity descriptors accounted for ,70% of the permeation information conveyed by the descriptors. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:4069,4084, 2009 [source] | ||||||||||