期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 50, 期 7, 页码 1275-1283出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci1000828
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资金
- Estonian Ministry of Education and Research [SF0140031Bs09]
- Estonian Science Foundation [7709]
- European Union, through the Center of Excellence in Chemical Biology, Estonia
- Kenan Foundation
Principal component analysis (PCA) of a large data matrix (153 solvents x 396 solutes) for Ostwald solubility coefficients (log L) resulted in a two-component model covering 98.6% of the variability. Analysis of the principal components exposed the structural characteristics of solutes and solvents that codify interactions which determine the behavior of a chemical in the surrounding media. The pattern revealed by PCA analysis distinguishes solutes according to the molecular size, functional groups, and electrostatic interactions, such as polarity and hydrogen-bonding donor and acceptor properties.
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