Results 141 to 150 of about 2,775 (180)

QSPR for Nonionic Surfactants

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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Automated QSPR through Competitive Workflow

Journal of Computer-Aided Molecular Design, 2005
This paper describes a novel software architecture, Competitive Workflow, which implements workflow as a distributed and competitive multi-agent system. The implementation of a competitive workflow architecture designed to model important computer-aided molecular design workflows, the Discovery Bus, is described.
Leahy DE, Cartmell J, Enoch S, Krstajic D, Bowen J   +4 more
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QSPR modeling of UV absorption intensities

Journal of Computer-Aided Molecular Design, 2007
Literature 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, Svetoslav H. Slavov, Dimitar A. Dobchev, Mati Karelson   +3 more
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Multimolecular polyhedra and QSPR

Journal of Mathematical Chemistry, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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QSPR prediction of densities of organic liquids

Computers & Chemistry, 1999
Abstract 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 of alkenes

2003
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Cvek, Josipa, Jurišić, Blaženka, Nikolić, Sonja, Seybold, Paul G.   +3 more
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QSAR/QSPR Methods

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, Supratik Kar, Rudra Narayan Das   +2 more
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y-Randomization and Its Variants in QSPR/QSAR

Journal of Chemical Information and Modeling, 2007
y-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, Gerta Rücker, Markus Meringer   +2 more
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Modified Connectivity Indices and Their Application to QSPR Study

Journal of Chemical Information and Computer Sciences, 2003
A modified adjacency matrix was developed to delineate the chemical graph of a compound, in which the element a(ii) along the diagonal of the matrix reflects the numbers of the lone-pair electrons and pi bonds of the ith atom, and the off-diagonal element a(ij) of the matrix characterizes whether the jth non-hydrogen atom is bonded to the ith non ...
Chunsheng Yang, Chongli Zhong
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A New Topological Index for QSPR of Alkanes

Journal of Chemical Information and Computer Sciences, 1998
A new topological index Xu based on the adjacency matrix A and the distance matrix D is derived in this paper. The index, which is very simple to calculate and also has good discrimination of alkane isomers, is used to correlate with the selected physicochemical properties of a wide range of alkanes.
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