Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase

Journal of Computer-Aided Molecular Design

Volumn 20, Issue 3, 2006, Pages 145-157

  Szaleniec, Maciej ^a   Witko, Małgorzata ^a   Tadeusiewicz, Ryszard ^b   Goclon, Jakub ^a  

^a INSTITUTE OF CATALYSIS AND SURFACE CHEMISTRY   (Poland)

^b AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

Author keywords

Artificial neural network modeling; DFT; Enzyme activity; Ethylbenzene dehydrogenase; Multiple linear regression; QSAR

Indexed keywords

COMPUTATIONAL CHEMISTRY; DENSITY FUNCTIONAL THEORY; ENZYME ACTIVITY; ETHYLBENZENE; FORECASTING; NEURAL NETWORKS; QUANTUM CHEMISTRY; REACTION RATES;

ARTIFICIAL NEURAL NETWORK MODELING; CATALYSE; DENSITY-FUNCTIONAL-THEORY; ENZYMATIC ACTIVITIES; ENZYMES ACTIVITY; ETHYLBENZENE DEHYDROGENASE; ITS APPLICATIONS; MULTIPLE LINEAR REGRESSIONS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; SPECIFIC OXIDATION;

MULTIPLE LINEAR REGRESSION;

ALCOHOL DERIVATIVE; ETHYLBENZENE; ETHYLBENZENE DEHYDROGENASE; OXIDOREDUCTASE; UNCLASSIFIED DRUG;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AZOARCUS; CALCULATION; CATALYSIS; CLASSIFICATION; CONTROLLED STUDY; CORRELATION ANALYSIS; DENSITY FUNCTIONAL THEORY; DRUG INDUSTRY; ENZYME ACTIVITY; ENZYME KINETICS; ENZYME METABOLISM; ENZYME STRUCTURE; ENZYME SUBSTRATE; LINEAR REGRESSION ANALYSIS; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTUM CHEMISTRY; SENSITIVITY ANALYSIS; SPECTROPHOTOMETRY; STEREOSPECIFICITY; THEORETICAL MODEL; THREE DIMENSIONAL IMAGING;

EID: 33747688457     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-006-9042-6     Document Type: Article

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