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Development of QSPR Strategy for the Solubility Prediction
Current Computer-Aided Drug Design, 2018QSPR modelling is one of the major computational tools used to correlate molecular characteristics with physiochemical properties of molecules. In present work, QSPR models are formed using AIC and VIF multicollinearity indicators for descriptors selection taking solubility data of Paclitaxel prodrugs. Geometry optimization of these Paclitaxel prodrugs
Nupur S, Munjal +2 more
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Multimolecular polyhedra and QSPR
Journal of Mathematical Chemistry, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Journal of Dispersion Science and Technology, 2007
A quantitative structure property relationship; QSPR was preformed as a means to predict critical micelle concentration of nonionic surfactants via correlating properties to parameters calculated from molecular structure. Such parameters; molecular weight, M w , hydrophobic‐hydrophilic fragments molecular weight ratio, χ, polarizability, α, partition ...
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A quantitative structure property relationship; QSPR was preformed as a means to predict critical micelle concentration of nonionic surfactants via correlating properties to parameters calculated from molecular structure. Such parameters; molecular weight, M w , hydrophobic‐hydrophilic fragments molecular weight ratio, χ, polarizability, α, partition ...
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QSPR prediction of densities of organic liquids
Computers & Chemistry, 1999Abstract A general quantitative structure properly relationship (QSPR) treatment of a data set incorporating 303 individual structures (containing C, H, N, O, S, F, Cl, Br and I) from a wide cross section of classes of organic liquids has given an excellent two-parameter correlation for densities (R2=0.9749, s2=0.0021 for ρ20).
Mati Karelson, Anti Perkson
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QSPR modeling of UV absorption intensities
Journal of Computer-Aided Molecular Design, 2007Literature UV absorption intensities at 260 nm and 25 degrees C in water of a diverse set of 805 organic compounds when analyzed by CODESSA Pro software using an initial pool of 800 + descriptors provide a significant QSPR correlation (R (2) = 0.692). Concurrently, a neural networks approach was used to develop a corresponding nonlinear model.
Alan R. Katritzky +3 more
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2015
QSAR/QSPR analysis started with different classical approaches constituting the core concept of predictive modeling analysis in the context of structure–activity relationships. Such classical techniques have been based on various postulates and hypotheses.
Kunal Roy +2 more
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QSAR/QSPR analysis started with different classical approaches constituting the core concept of predictive modeling analysis in the context of structure–activity relationships. Such classical techniques have been based on various postulates and hypotheses.
Kunal Roy +2 more
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On the Use of Neural Network Ensembles in QSAR and QSPR
Journal of Chemical Information and Computer Sciences, 2002Despite their growing popularity among neural network practitioners, ensemble methods have not been widely adopted in structure-activity and structure-property correlation. Neural networks are inherently unstable, in that small changes in the training set and/or training parameters can lead to large changes in their generalization performance.
Dimitris K. Agrafiotis +2 more
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y-Randomization and Its Variants in QSPR/QSAR
Journal of Chemical Information and Modeling, 2007y-Randomization is a tool used in validation of QSPR/QSAR models, whereby the performance of the original model in data description (r2) is compared to that of models built for permuted (randomly shuffled) response, based on the original descriptor pool and the original model building procedure. We compared y-randomization and several variants thereof,
Christoph Rücker +2 more
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4D-Fingerprints, Universal QSAR and QSPR Descriptors
Journal of Chemical Information and Computer Sciences, 2004An elusive goal in the field of chemoinformatics and molecular modeling has been the generation of a set of descriptors that, once calculated for a molecule, may be used in a wide variety of applications. Since such universal descriptors are generated free from external constraints, they are inherently independent of the data set in which they are ...
Craig L. Senese +4 more
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