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Prediction of C<INF>60</INF> Solubilities from Solvent Molecular Structures
- 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 × 10<SUP>4</SUP>)(mole fraction of C<INF>60</INF> 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