کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1180020 | 962822 | 2010 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Counter-propagation artificial neural networks as a tool for prediction of pKBH+ for series of amides
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
One of the best models, discussed in details in the article, has only three interpretable descriptors: number of halogen atoms in the structure, the energy of the lowest unoccupied molecular orbital (LUMO) which reflects the electronic properties of the molecules and the average molecular weight. The final analysis of this model shows that the most responsible for modeling of the pKBH+ values is the number of the present halogen atoms in the structures. Similar relative importance has LUMO. This descriptor helps in groping of the similar substances in different part of the CPANN. While the average molecular weight, with nearly seven times smaller relative importance compared to previous two descriptors, is related to the influence of the presence of, in most of the cases, more than one halogen atom in the structures on pKBH+. Finally, the developed models have excellent generalization performances which were checked using independent test set.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 102, Issue 2, 15 July 2010, Pages 123-129
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 102, Issue 2, 15 July 2010, Pages 123-129
نویسندگان
Goran StojkoviÄ, Marjana NoviÄ, Igor Kuzmanovski,