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Prediction of C<INF>60</INF> Solubilities from Solvent Molecular Structures

Authors :
Danauskas, S. M.
Jurs, P. C.
Source :
Journal of Chemical Information and Computer Sciences (now called Journal of Chemical Information and Modeling); March 26, 2001, Vol. 41 Issue: 2 p419-424, 6p
Publication Year :
2001

Abstract

Models predicting fullerene solubility in 96 solvents at 298 K were developed using multiple linear regression and feed-forward computational neural networks (CNN). The data set consisted of a diverse set of solvents with solubilities ranging from −3.00 to 2.12 log (solubility) where solubility = (1 &#215; 10&lt;SUP&gt;4&lt;/SUP&gt;)(mole fraction of C&lt;INF&gt;60&lt;/INF&gt; in saturated solution). Each solvent was represented by calculated molecular structure descriptors. A pool of the best linear models, as determined by rms error, was developed, and a CNN model was developed for each of the linear models. The best CNN model was chosen based on the lowest value of a specified cost function and had an architecture of 9−3−1. The 76-compound training set for this model had a root-mean-square error of 0.255 log solubility units, while the 10-compound cross-validation set had an rms error of 0.253. The 10-compound external prediction set had an rms error of 0.346 log solubility units.

Details

Language :
English
ISSN :
00952338 and 15205142
Volume :
41
Issue :
2
Database :
Supplemental Index
Journal :
Journal of Chemical Information and Computer Sciences (now called Journal of Chemical Information and Modeling)
Publication Type :
Periodical
Accession number :
ejs1108243