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Volumn 12, Issue 3, 2006, Pages 338-347
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Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR
a
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Author keywords
Acidity constant; Artificial neural networks; Phenols; Quantitative structure activity relationship; Theoretical descriptors
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Indexed keywords
2 ACETYLPHENOL;
2 ALLYLPHENOL;
2 BROMOPHENOL;
2 CHLOROPHENOL;
2 HYDROXYBIPHENYL;
2 NITROPHENOL;
2,3 DICHLOROPHENOL;
2,4 DICHLOROPHENOL;
2,4,5 TRICHLOROPHENOL;
3 HYDROXYBENZALDEHYDE;
3,4 DICHLOROPHENOL;
3,5 DICHLOROPHENOL;
4 BROMOPHENOL;
4 CHLOROPHENOL;
4 HYDROXYBENZALDEHYDE;
4 HYDROXYBIPHENYL;
4 NITROPHENOL;
CATECHOL;
HYDROXYL GROUP;
META CRESOL;
ORTHO CRESOL;
OXYGEN;
PARA CRESOL;
PHENOL DERIVATIVE;
PHLOROGLUCINOL;
PROTON;
PYROGALLOL;
RESORCINOL;
TERT BUTYL ALCOHOL;
UNCLASSIFIED DRUG;
ACIDITY;
ANALYTICAL ERROR;
AQUEOUS SOLUTION;
ARTICLE;
ARTIFICIAL NEURAL NETWORK;
CHEMICAL STRUCTURE;
COMPUTER PROGRAM;
CONTROLLED STUDY;
CORRELATION COEFFICIENT;
HYDROGEN BOND;
LINEAR REGRESSION ANALYSIS;
MATHEMATICAL COMPUTING;
MOLECULAR MODEL;
MOLECULAR WEIGHT;
NONLINEAR SYSTEM;
POLARIZATION;
PREDICTION;
PRIORITY JOURNAL;
PROCESS OPTIMIZATION;
QUANTITATIVE STRUCTURE ACTIVITY RELATION;
ACIDS;
HYDROGEN-ION CONCENTRATION;
MODELS, CHEMICAL;
NEURAL NETWORKS (COMPUTER);
PHENOLS;
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;
TEMPERATURE;
WATER;
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EID: 33644754097
PISSN: 16102940
EISSN: None
Source Type: Journal
DOI: 10.1007/s00894-005-0050-6 Document Type: Article |
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Times cited : (45)
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References (37)
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