Paper Info
Title
GEMPLS: a new QSAR method combining generic evolutionary method and partial least squares
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.
Year
DOI
Venue
2005
10.1007/978-3-540-32003-6_13
EvoWorkshops
Keywords
Field
DocType
cross-validated correlation coefficient,analysis system,mahalanobis distance,generic evolutionary method,new qsar method,hiv-1 protease,new method,new genetic operator,qsar model,genetic evolutionary method,comparative binding energy,best lv,genetics,latent variable,binding energy,cross validation,quantitative structure activity relationship,genetic operator
Least squares,Quantitative structure–activity relationship,Genetic operator,Evolutionary algorithm,Partial least squares regression,Mahalanobis distance,Artificial intelligence,Genetic algorithm,Discrete mathematics,Pattern recognition,Algorithm,Mathematics,SDEP
Conference
Volume
ISSN
ISBN
3449
0302-9743
3-540-25396-3
Citations 
PageRank 
References 
0
0.34
2
Authors
4
Name
Order
Citations
PageRank
Yen-chih Chen1222.82
Jinn-moon Yang236435.89
Chi-Hung Tsai31168.70
Cheng-yan Kao458661.50