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Prediction of Molecular Properties by Recursive Neural Networks: Application to the glass transition temperature of acrylic polymers - Softcover
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In the past few years, a novel approach in cheminformatics for the Quantitative Structure-Property Relationship (QSPR) analysis of physical, chemical and biological properties of chemical compounds was developed at the University of Pisa. This methodology is based on the direct treatment of molecular structure, without using numerical descriptors, and employs recursive neural networks. In subsequent studies it was successfully used to predict various properties of different classes of compounds. It is a promising tool in the evaluation of existing substances, as well as in the design of new materials. This master thesis focuses on the prediction of the properties of polymers, a problem not easily treatable with traditional methods based on molecular descriptors. The study explores different representational issues and show the accuracy and flexibility of the structure-based QSPR approach.
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Prediction of Molecular Properties by Recursive Neural Networks
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3639162099
ISBN 13: 9783639162097
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Prediction of Molecular Properties by Recursive Neural Networks
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VDM Verlag Dr. Müller, 2010
ISBN 10: 3639162099
ISBN 13: 9783639162097
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bertinetto Carlo GiuseppeCarlo Giuseppe Bertinetto was born on 7-12-1981 in Torino, Italy. In October 2006 he graduated cum laude in chemistry at the University of Pisa. He is currently a PhD student in chemical sciences at the Un. Seller Inventory # 4963031
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Prediction of Molecular Properties by Recursive Neural Networks
Published by
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3639162099
ISBN 13: 9783639162097
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Softcover
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Condition: New. Print on Demand pp. 116 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Seller Inventory # 131806550
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Prediction of Molecular Properties by Recursive Neural Networks : Application to the glass transition temperature of acrylic polymers
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the past few years, a novel approach in cheminformatics for the Quantitative Structure-Property Relationship (QSPR) analysis of physical, chemical and biological properties of chemical compounds was developed at the University of Pisa. This methodology is based on the direct treatment of molecular structure, without using numerical descriptors, and employs recursive neural networks. In subsequent studies it was successfully used to predict various properties of different classes of compounds. It is a promising tool in the evaluation of existing substances, as well as in the design of new materials. This master thesis focuses on the prediction of the properties of polymers, a problem not easily treatable with traditional methods based on molecular descriptors. The study explores different representational issues and show the accuracy and flexibility of the structure-based QSPR approach. Seller Inventory # 9783639162097
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Prediction of Molecular Properties by Recursive Neural Networks
Published by
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3639162099
ISBN 13: 9783639162097
New
Softcover
Seller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 4 out of 5 stars
Condition: New. PRINT ON DEMAND pp. 116. Seller Inventory # 18128748163
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Prediction of Molecular Properties by Recursive Neural Networks | Application to the glass transition temperature of acrylic polymers
Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Prediction of Molecular Properties by Recursive Neural Networks | Application to the glass transition temperature of acrylic polymers | Carlo Giuseppe Bertinetto | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639162097 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 101492011