Pregled bibliografske jedinice broj: 312157
Modeling anti-HIV activity of HEPT derivatives revisited. Multiregression models are not inferior ones
Modeling anti-HIV activity of HEPT derivatives revisited. Multiregression models are not inferior ones // Special Volume of the American Institute of Physics (AIP) - Conference Proceedings of ICCMSE 2007 / Simos, Theodore (ur.).
Melville (NY): American Institute of Physics (AIP), 2007. str. 521-523 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 312157 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Modeling anti-HIV activity of HEPT derivatives
revisited. Multiregression models are not
inferior ones
Autori
Bašic, Ivan ; Nadramija, Damir ; Flajšlik, Mario ; Amić, Dragan ; Lučić, Bono
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Special Volume of the American Institute of Physics (AIP) - Conference Proceedings of ICCMSE 2007
/ Simos, Theodore - Melville (NY) : American Institute of Physics (AIP), 2007, 521-523
Skup
International Conference of Computational Methods in Sciences and Engineering 2007
Mjesto i datum
Krf, Grčka, 25.09.2007. - 30.09.2007
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
HEPT derivatives ; anti-HIV activity ; QSAR models ; descriptor selection ; multivariate regression ; artificial neural networks ; non-linear relationships
Sažetak
Several quantitative structure-activity studies for this data set containing 107 HEPT derivatives have been performed since 1997, using the same set of molecules by (more or less) different classes of molecular descriptors. Multivariate Regression (MR) and Artificial Neural Network (ANN) models were developed and in each study the authors concluded that ANN models are superior to MR ones. We re-calculated multivariate regression models for this set of molecules using the same set of descriptors, and compared our results with the previous ones. Two main reasons for overestimation of the quality of the ANN models in previous studies comparing with MR models are: (1) wrong calculation of leave-one-out (LOO) cross-validated (CV) correlation coefficient for MR models in Luco et al., J. Chem. Inf. Comput. Sci. 37 392-401(1997), and (2) incorrect estimation/interpretation of leave-one-out (LOO) cross-validated and predictive performance and power of ANN models. More precise and more fair comparison of fit and LOO CV statistical parameters shows that MR models are more stable. In addition, MR models are much simpler than ANN ones. For real testing the predictive performance of both classes of models we need more HEPT derivatives, because all ANN models that presented results for external set of molecules used experimental values in optimization of modeling procedure and model parameters.
Izvorni jezik
Engleski
Znanstvena područja
Kemija
POVEZANOST RADA
Projekti:
MZOS-098-1770495-2919 - Razvoj metoda za modeliranje svojstava bioaktivnih molekula i proteina (Lučić, Bono, MZOS ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Fakultet agrobiotehničkih znanosti Osijek,
Institut "Ruđer Bošković", Zagreb