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Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development
International Journal of Computer Aided Engineering and Technology, 19 (2006), 12; 835-855. https://doi.org/10.1007/s10822-005-9022-2
Debeljak, Željko; Marohnić, Viktor; Srečnik, Goran; Medić-Šarić, Marica

Institucijski repozitorij: Repozitorij KBC Osijek

Citirajte ovaj rad

Debeljak, Ž., Marohnić, V., Srečnik, G. i Medić Šarić, M. (2006). Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development. International Journal of Computer Aided Engineering and Technology, 19. (12), 835-855. doi: 10.1007/s10822-005-9022-2

Debeljak, Željko, et al. "Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development." International Journal of Computer Aided Engineering and Technology, vol. 19, br. 12, 2006, str. 835-855. https://doi.org/10.1007/s10822-005-9022-2

Debeljak, Željko, Viktor Marohnić, Goran Srečnik i Marica Medić Šarić. "Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development." International Journal of Computer Aided Engineering and Technology 19, br. 12 (2006): 835-855. https://doi.org/10.1007/s10822-005-9022-2

Debeljak, Ž., et al. (2006) 'Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development', International Journal of Computer Aided Engineering and Technology, 19(12), str. 835-855. doi: 10.1007/s10822-005-9022-2

Debeljak Ž, Marohnić V, Srečnik G, Medić Šarić M. Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development. International Journal of Computer Aided Engineering and Technology [Internet]. 2006. [pristupljeno 28.12.2024.];19(12):835-855. doi: 10.1007/s10822-005-9022-2

Ž. Debeljak, V. Marohnić, G. Srečnik i M. Medić Šarić, "Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development", International Journal of Computer Aided Engineering and Technology, vol. 19, br. 12, str. 835-855, 2006. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:239:165922. [Citirano: 28.12.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:239:165922
Podaci o radu
Naslov (engleski)Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development
AutorŽeljko Debeljak
MBZ: 296571
AutorViktor Marohnić
AutorGoran Srečnik
AutorMarica Medić-Šarić
MBZ: 74265
Autorova ustanova(Klinički bolnički centar Osijek)
Autorova ustanovaSveučilište u Zagrebu
Farmaceutsko-biokemijski fakultet
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Neurologija
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Farmacija
Medicinska biokemija
Sažetak (engleski)
Capability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh's filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaInternational Journal of Computer Aided Engineering and Technology
Brojčani podacivol. 19, br. 12, str. 835-855
p-ISSN1757-2657
e-ISSN1757-2665
DOIhttps://doi.org/10.1007/s10822-005-9022-2
URN:NBNurn:nbn:hr:239:165922
Datum objave publikacije2006
URL dokumentahttps://link.springer.com/article/10.1007/s10822-005-9022-2
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2020-05-14 08:57:07