title: A general 13C NMR spectrum predictor using data mining techniques.
abstract: A general-case neural network model for 13C NMR spectrum prediction (estimation) was built from more than 8,300 carbon atoms having various environments. Building the model from the data set required a few weeks' work using commercial software. Average deviation on test data is ca. 4 ppm. There is no limit on molecule complexity. Estimation error does not depend on molecule size or complexity. The emphasis is on the data, the method and the results, not on the processes that take place inside the modelling software. Advantages, disadvantages and peculiarities of neural network-based data modelling ("data mining") are described at length. The differences in data handling between the data mining approach and traditional statistical modelling techniques are discussed and illustrated in detail. The spectrum predictor is available from PMSI at no charge.
authors.affiliation: Department of Research, PMSI, Paris, France.
authors.forename: C
authors.initials: C
authors.lastname: Le Bret
mesh_terms: D002247:Carbon Isotopes / Q000737:chemistry; D005544:Forecasting; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
chemical_list: D002247:Carbon Isotopes