کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9652956 | 677100 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modelling expertise for structure elucidation in organic chemistry using Bayesian networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 18, Issues 4â5, August 2005, Pages 207-215
Journal: Knowledge-Based Systems - Volume 18, Issues 4â5, August 2005, Pages 207-215
نویسندگان
Michaela Hohenner, Sven Wachsmuth, Gerhard Sagerer,