caa a22 4500 467938830 CHVBK 20180406153006.0 cr unu---uuuuu 170328e20060301xx s 000 0 eng 10.1007/s10822-006-9042-6 doi (NATIONALLICENCE)springer-10.1007/s10822-006-9042-6 Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase [Elektronische Daten] [Maciej Szaleniec, Małgorzata Witko, Ryszard Tadeusiewicz, Jakub Goclon] Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure-activity relationship (QSAR) approaches. Springer Science+Business Media, Inc. 2006, 2006 Artificial neural network modeling nationallicence DFT nationallicence Enzyme activity nationallicence Ethylbenzene dehydrogenase nationallicence Multiple linear regression nationallicence QSAR nationallicence Szaleniec Maciej Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239, Krakow, Poland aut Witko Małgorzata Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239, Krakow, Poland aut Tadeusiewicz Ryszard AGH University of Science and Technology, al.Mickiewicza 30, 30-059, Krakow, Poland aut Goclon Jakub Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239, Krakow, Poland aut Journal of Computer-Aided Molecular Design Kluwer Academic Publishers 20/3(2006-03-01), 145-157 0920-654X 20:3<145 2006 20 10822 https://doi.org/10.1007/s10822-006-9042-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10822-006-9042-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Szaleniec Maciej Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239, Krakow, Poland aut NATIONALLICENCE 700 1- Witko Małgorzata Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239, Krakow, Poland aut NATIONALLICENCE 700 1- Tadeusiewicz Ryszard AGH University of Science and Technology, al.Mickiewicza 30, 30-059, Krakow, Poland aut NATIONALLICENCE 700 1- Goclon Jakub Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239, Krakow, Poland aut NATIONALLICENCE 773 0- Journal of Computer-Aided Molecular Design Kluwer Academic Publishers 20/3(2006-03-01), 145-157 0920-654X 20:3<145 2006 20 10822 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer