scholarly journals Performance of the ALOGPS 2.1 program for octanol-water partition coefficient prediction with organic chemicals on the Canadian Domestic Substances List

2010 ◽  
Author(s):  
Sierra Rayne ◽  
Kaya Forest
Keyword(s):  
Partition Coefficient ◽  
Organic Chemicals ◽  
Water Partition Coefficient

Factors Affecting Bioconcentration of Trace Organic Contamination in Waters

1989 ◽  
Vol 21 (2) ◽  
pp. 147-150 ◽  
Author(s):  
D. W. Hawker ◽  
D. W. Connell
Keyword(s):  
Molecular Weight ◽  
Bioconcentration Factor ◽  
Aqueous Solubility ◽  
Organic Chemicals ◽  
Organic Contamination ◽  
Hydrophobic Organic Chemicals ◽  
Factors Affecting ◽  
Water Partition Coefficient ◽  
Physicochemical Factors ◽  
Trace Organic

The influence of some important biological and physicochemical factors on the bioconcentration of hydrophobic organic chemicals is outlined. For non-ionizable, persistent compounds the bioconcentration factor can be related to a compound's octanol/water partition coefficient, aqueous solubility and molecular weight, while the lipid content of an organism also affects the bioconcentration potential of these compounds. The effect of ionization and biodegradation of organic chemicals on bioconcentration is also discussed.

scholarly journals QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

2009 ◽  
Vol 7 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Emilio Benfenati
Keyword(s):  
Partition Coefficient ◽  
Organometallic Compounds ◽  
Applicability Domain ◽  
Training Set ◽  
Input Line ◽  
Test Set ◽  
Water Partition Coefficient ◽  
Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

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