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Title: Octanol/water partition coefficient estimation without missing fragment problems

Abstract

Atom/fragment contribution values, used to estimate the log octanol-water partition coefficient (log P) of organic compounds, have been determined for 125 simple chemical substructures by a multiple linear regression of 1175 compounds with measured log P values. An additional 1176 compounds were used to determine 230 ``correction factors`` for various substructure orientations. Log P of a compound is estimated by simply summing all atom-fragment contribution values and correction factors occurring in a chemical structure. For the 2351 compound training set, the correlation coefficient (r{sup 2}) for the estimated to measured log P values is 0.98 with a standard deviation (sd) of 0.22 and an absolute mean error (me) of 0.16. This atom/fragment contribution (AFC) method was then tested on a separate validation set of 6,055 measured log P value.% that were not used to derive the methodology, and yielded r{sup 2} of 0.94, ad of 0.41 and me of 0.31. The method is able to predict log P within {+-}0.8 log units for over 96% of the experimental dataset of 8,406 compounds. Because of the simple atom/fragment methodology, ``missing fragments`` (a problem encountered in other methods), does not occur in the AFC method. Statistically, it is superior to other comprehensivemore » estimation methods.« less

Authors:
;  [1]
  1. Syracuse Research Corp., NY (United States)
Publication Date:
OSTI Identifier:
49564
Report Number(s):
CONF-9410273-
TRN: IM9523%%387
Resource Type:
Conference
Resource Relation:
Conference: 15. annual meeting of the Society of Environmental Toxicology and Chemistry (SETAC), Denver, CO (United States), 30 Oct - 3 Nov 1994; Other Information: PBD: 1994; Related Information: Is Part Of Society of Environmental Toxicology and Chemistry 15th annual meeting: Abstract book. Ecological risk: Science, policy, law, and perception; PB: 286 p.
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ORGANIC COMPOUNDS; WATER CHEMISTRY; WATER POLLUTION; REGRESSION ANALYSIS; MATHEMATICAL MODELS

Citation Formats

Meylan, W, and Howard, P H. Octanol/water partition coefficient estimation without missing fragment problems. United States: N. p., 1994. Web.
Meylan, W, & Howard, P H. Octanol/water partition coefficient estimation without missing fragment problems. United States.
Meylan, W, and Howard, P H. 1994. "Octanol/water partition coefficient estimation without missing fragment problems". United States.
@article{osti_49564,
title = {Octanol/water partition coefficient estimation without missing fragment problems},
author = {Meylan, W and Howard, P H},
abstractNote = {Atom/fragment contribution values, used to estimate the log octanol-water partition coefficient (log P) of organic compounds, have been determined for 125 simple chemical substructures by a multiple linear regression of 1175 compounds with measured log P values. An additional 1176 compounds were used to determine 230 ``correction factors`` for various substructure orientations. Log P of a compound is estimated by simply summing all atom-fragment contribution values and correction factors occurring in a chemical structure. For the 2351 compound training set, the correlation coefficient (r{sup 2}) for the estimated to measured log P values is 0.98 with a standard deviation (sd) of 0.22 and an absolute mean error (me) of 0.16. This atom/fragment contribution (AFC) method was then tested on a separate validation set of 6,055 measured log P value.% that were not used to derive the methodology, and yielded r{sup 2} of 0.94, ad of 0.41 and me of 0.31. The method is able to predict log P within {+-}0.8 log units for over 96% of the experimental dataset of 8,406 compounds. Because of the simple atom/fragment methodology, ``missing fragments`` (a problem encountered in other methods), does not occur in the AFC method. Statistically, it is superior to other comprehensive estimation methods.},
doi = {},
url = {https://www.osti.gov/biblio/49564}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Dec 31 00:00:00 EST 1994},
month = {Sat Dec 31 00:00:00 EST 1994}
}

Conference:
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