Rapid Method for the Estimation of Octanol / Water Partition Coefficient (Log Poct) from Gradient RP-HPLC Retention and a Hydrogen Bond Acidity Term (Sigma alpha2H)

2001 ◽  
Vol 8 (9) ◽  
pp. 1137-1146 ◽  
Author(s):  
Klara Valko ◽  
Chau Du ◽  
Christopher Bevan ◽  
Derek Reynolds ◽  
Michael Abraham
Keyword(s):  
Hydrogen Bond ◽  
Partition Coefficient ◽  
Rapid Method ◽  
Rp Hplc ◽  
Water Partition Coefficient ◽  
Hydrogen Bond Acidity

Determination of n-octanol/water partition coefficient for DDT-related compounds by RP-HPLC with a novel dual-point retention time correction

Chemosphere ◽  
2011 ◽  
Vol 83 (2) ◽  
pp. 131-136 ◽  
Author(s):  
Shu-ying Han ◽  
Jun-qin Qiao ◽  
Yun-yang Zhang ◽  
Li-li Yang ◽  
Hong-zhen Lian ◽  
...  
Keyword(s):  
Partition Coefficient ◽  
Retention Time ◽  
Rp Hplc ◽  
Water Partition Coefficient ◽  
Time Correction ◽  
Related Compounds

Octadecyl, Phenyl and Cyano Phases Comparison for the RP-HPLC Prediction of Octanol-Water Partition Coefficient

1987 ◽  
Vol 10 (6) ◽  
pp. 1065-1075 ◽  
Author(s):  
M. C. Pietrogrande ◽  
F. Dondi ◽  
G. Blo ◽  
P. A. Borea ◽  
C. Bighi
Keyword(s):  
Partition Coefficient ◽  
Rp Hplc ◽  
Water Partition Coefficient

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.

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