Chemical reactivity predictions: Use of data mining techniques for analyzing regioselective azidolysis of epoxides

Journal of Computational Chemistry

Volumn 31, Issue 14, 2010, Pages 2612-2619

  Borghini, Alice ^a   Crotti, Paolo ^a   Pietra, Daniele ^a   Favero, Lucilla ^a   Bianucci, Anna Maria ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

azidolysis; chemical reactivity prediction; Gaussian Process regression; k nearest neighbor; locally weighted regression; multiple linear regression; oxiranes; quantitative structure property relationships (QSPR)

Indexed keywords

AZIDOLYSIS; CHEMICAL REACTIVITY PREDICTION; GAUSSIAN PROCESS REGRESSION; K-NEAREST NEIGHBORS; LOCALLY WEIGHTED REGRESSION; MULTIPLE LINEAR REGRESSIONS; OXIRANES; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS;

ALCOHOLS; DATA MINING; FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LITHIUM; REGIOSELECTIVITY; SODIUM; SULFUR COMPOUNDS;

LINEAR REGRESSION;

AMINOALCOHOL; EPOXIDE; LITHIUM DERIVATIVE; LITHIUM PERCHLORATE; PERCHLORIC ACID; SODIUM AZIDE;

ALGORITHM; ARTICLE; CATALYSIS; CHEMISTRY; COMPUTER SIMULATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STEREOISOMERISM; SYNTHESIS;

ALGORITHMS; AMINO ALCOHOLS; CATALYSIS; COMPUTER SIMULATION; EPOXY COMPOUNDS; LITHIUM COMPOUNDS; PERCHLORIC ACID; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SODIUM AZIDE; STEREOISOMERISM;

EID: 78149460671     PISSN: 01928651     EISSN: 1096987X     Source Type: Journal    
DOI: 10.1002/jcc.21556     Document Type: Article

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