The Effect of Changes in Acidity and Salinity on the Octanol Water Partition Coefficient of Monomethylmercuric Species Present in Aquatic Environments at High pE.

1991 ◽  
Author(s):  
Michael A. Major
Keyword(s):  
Partition Coefficient ◽  
Aquatic Environments ◽  
Water Partition Coefficient

A model for predicting sediment-water partition of toxic chemicals in aquatic environments

2002 ◽  
Vol 46 (11-12) ◽  
pp. 437-442 ◽  
Author(s):  
H. Shimazu ◽  
E. Ohnishi ◽  
N. Ozaki ◽  
T. Fukushima ◽  
O. Nakasugi
Keyword(s):  
Organic Matter ◽  
Partition Coefficient ◽  
Water Bodies ◽  
Toxic Chemicals ◽  
Published Data ◽  
Aquatic Environments ◽  
Inorganic Matter ◽  
Water Partition Coefficient ◽  
Japanese Water ◽  
Order Of Magnitude

In order to investigate the characteristics of sediment-water partition of chemicals in aquatic environments using published data, we developed a model for predicting the sediment-water partition coefficient (Kp) as the sum of sorption to sediment organic matter and sorption to sediment inorganic matter. This model is so successful that the differences between Kp (median for a variety of Japanese water bodies) and pre-Kp (predicted Kp) are within one order of magnitude in 24 out of 28 chemicals.

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.

scholarly journals Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nadin Ulrich ◽  
Kai-Uwe Goss ◽  
Andrea Ebert
Keyword(s):  
Partition Coefficient ◽  
Data Augmentation ◽  
Environmental Chemistry ◽  
Chemical Properties ◽  
Predictive Performance ◽  
Chemical Structures ◽  
Tautomeric Forms ◽  
Learning Techniques ◽  
Water Partition Coefficient ◽  
Using Data

AbstractToday more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.

Rapid Estimation of Octanol–Water Partition Coefficient for Triazole Fungicides by MEKC with Sodium Deoxycholate as Surfactant

2008 ◽  
Vol 68 (5-6) ◽  
pp. 415-419 ◽  
Author(s):  
Wan Aini Wan Ibrahim ◽  
Dadan Hermawan ◽  
Mohamed Noor Hasan ◽  
Hassan Y. Aboul Enein ◽  
M. Marsin Sanagi
Keyword(s):  
Partition Coefficient ◽  
Sodium Deoxycholate ◽  
Rapid Estimation ◽  
Triazole Fungicides ◽  
Water Partition Coefficient

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

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