GEMPLS: a new QSAR method combining generic evolutionary method and partial least squares

Authors:
Yen-Chih Chen;Jinn-Moon Yang;Chi-Hung Tsai;Cheng-Yan Kao
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;Department of Biological Science and Technology & Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 2
Cited 0

Reducing bias and inefficiency in the selection algorithm

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Atomic charges derived from semiempirical methods

Journal of Computational Chemistry

Quantified Score

Hi-index	0.00

Visualization

Abstract

We 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.