QSPR analysis of copolymers by recursive neural networks: Prediction of the glass transition temperature of (meth)acrylic random copolymers

Molecular Informatics

Volumn 29, Issue 8-9, 2010, Pages 635-643

  Bertinett, Carlo Giuseppe ^a   Duce, Celia ^a   Micheli, Alessio ^a   Solaro, Roberto ^a   Tiné, Maria Rosaria ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Glass transition temperature; Phase transitions; Polymers; QSAR QSPR; Recursive neural network

Indexed keywords

COMPUTATIONAL CHEMISTRY; FORECASTING; GLASS; NEURAL NETWORKS; STATISTICAL TESTS; TEMPERATURE;

DATA SET; DESCRIPTORS; GLASS TRANSITION TEMPERATURE TG; LABELLED TREES; NEURAL NETWORK PREDICTIONS; QSAR/QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; RANDOM COPOLYMER; RECURSIVE NEURAL NETWORKS; RELATIONSHIP ANALYSIS;

GLASS TRANSITION;

ACRYLIC ACID COPOLYMER; COPOLYMER; POLYMER;

ARTICLE; CHEMICAL STRUCTURE; GLASS TRANSITION TEMPERATURE; MACHINE LEARNING; MACROMOLECULE; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE PROPERTY RELATION; RECURSIVE NEURAL NETWORK; STEREOCHEMISTRY; TECHNOLOGY; TEMPERATURE DEPENDENCE;

EID: 78650195945     PISSN: 18681743     EISSN: 18681751     Source Type: Journal    
DOI: 10.1002/minf.201000079     Document Type: Article

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