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Predicting hydration free energies of polychlorinated aromatic compounds from the SAMPL-3 data set with FiSH and LIE models.
Traian Sulea
Enrico O. Purisima
Published in:
J. Comput. Aided Mol. Des. (2012)
Keyphrases
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data sets
experimental data
real world
higher order
training set
high dimensional data
computational models
database
case study
training data
graph cuts
gene expression data