Paper Info
Title
Modelling expertise for structure elucidation in organic chemistry using Bayesian networks
Abstract
The development of automated methods for chemical synthesis as well as for chemical analysis has inundated chemistry with huge amounts of experimental data. To refine them into information, the field of chemoinformatics applies techniques from artificial intelligence, pattern recognition and machine learning. A key task concerning organic chemistry is structure elucidation. NMR spectra have become accessible at low expenses of time and sample size, they also are predictable with good precision, and they are directly related to structural properties of the molecule. So the classical approach of ranking structure candidates by comparison of NMR spectra works well, but since the structural space is huge, more sophisticated approaches are in demand. Bayesian networks are promising in this concern, as they allow for contemplation in a dual way: provided an appropriate model, conclusions can be drawn from a given spectrum regarding the corresponding structure or vice versa, since the same interrelations hold in both directions. The development of such a model is documented, and first results are shown supporting the applicability of Bayesian networks to structure elucidation.
Year
DOI
Venue
2005
10.1016/j.knosys.2005.03.001
Knowl.-Based Syst.
Keywords
Field
DocType
chemical analysis,machine learning,chemical synthesis,sample size,pattern recognition,bayesian networks,organic chemistry,spectrum,bayesian network,artificial intelligent,nmr spectra,structure elucidation
Data mining,Experimental data,Ranking,Organic chemistry,Computer science,Bayesian network,Artificial intelligence,Machine learning,Cheminformatics
Journal
Volume
Issue
ISSN
18
4-5
0950-7051
Citations 
PageRank 
References 
2
0.41
4
Authors
3
Name
Order
Citations
PageRank
Michaela Hohenner120.41
Sven Wachsmuth226743.83
Gerhard Sagerer3830108.85