Hutchison, DavidDavidHutchisonKanade, TakeoTakeoKanadeKittler, JosefJosefKittlerKleinberg, Jon M.Jon M.KleinbergMattern, FriedemannFriedemannMatternMitchell, John C.John C.MitchellNaor, MoniMoniNaorNierstrasz, OscarOscarNierstraszRangan, C. PanduC. PanduRanganSteffen, BernhardBernhardSteffenSudan, MadhuMadhuSudanTerzopoulos, DemetriDemetriTerzopoulosTygar, DoughDoughTygarVardi, Moshe Y.Moshe Y.VardiWeikum, GerhardGerhardWeikum2009-04-292018-07-052009-04-292018-07-052005http://ntur.lib.ntu.edu.tw//handle/246246/154593We have proposed a new method for quantitative structure-activity relationship (QSAR) analysis. This tool, termed GEMPLS, combines a genetic evolutionary method with partial least squares (PLS). We designed a new genetic operator and used Mahalanobis distance to improve predicted accuracy and speed up a solution for QSAR. The number of latent variables (lv) was encoded into the chromosome of GA, instead of scanning the best lv for PLS. We applied GEMPLS on a comparative binding energy (COMBINE) analysis system of 48 inhibitors of the HIV-1 protease. Using GEMPLS, the cross-validated correlation coefficient (q2) is 0.9053 and external SDEP (SDEPex) is 0.61. The results indicate that GEMPLS is very comparative to GAPLS and GEMPLS is faster than GAPLS for this data set. GEMPLS yielded the QSAR models, in which selected residues are consistent with some experimental evidences. ? Springer-Verlag Berlin Heidelberg 2005.application/pdf223144 bytesapplication/pdfen-US[SDGs]SDG3[SDGs]SDG7Binding energy; Enzyme inhibition; Enzymes; Evolutionary algorithms; Genes; Mathematical models; Mathematical operators; Genetic evolutionary method; Genetic operator; Latent variables (lv); Partial least squares (PLS); Genetic engineeringjournal article10.1007/978-3-540-32003-6_13http://ntur.lib.ntu.edu.tw/bitstream/246246/154593/1/16.pdf