Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds-Reference-Cited by-同舟云学术

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Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds

Published:2022-12 Issue:6 Volume:10 Page:108552
ISSN:2213-3437
Container-title:Journal of Environmental Chemical Engineering
language:en
Short-container-title:Journal of Environmental Chemical Engineering

Author:

Kang Xuejing,Hu Bo,Perdana Mayang Christy,Zhao Yongsheng,Chen Zhongbing

Funder

Česká Zemědělská Univerzita v Praze

Czech University of Life Sciences Prague Faculty of Environmental Sciences

Publisher

Elsevier BV

Subject

Process Chemistry and Technology,Pollution,Waste Management and Disposal,Chemical Engineering (miscellaneous)

Reference55 articles.

1. Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine;Pan;J. Hazard. Mater.,2009

2. A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine;Kumar;J. Nat. Sci. Biol. Med.,2011

3. Physicochemical properties as useful tools for predicting the environmental fate of organic chemicals;Klöpffer;Ecotoxicol. Environ. Saf.,1982

4. Biological aspects involved in the degradation of organic pollutants;Diez;J. Soil Sci. Plant Nutr.,2010

5. Comparison of the octanol/water partition coefficients calculated by ClogP®, ACDlogP and KowWin® to experimentally determined values;Machatha;Int. J. Pharm.,2005

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3. Abraham Model Solute Descriptors for Favipiravir: Case of Tautomeric Equilibrium and Intramolecular Hydrogen-Bond Formation;Thermo;2023-08-10

4. Prediction of the n-Octanol/Water Partition Coefficients of Basic Compounds Using Multi-Parameter QSRR Models Based on IS-RPLC Retention Behavior in a Wide pH Range;Molecules;2023-02-28

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