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

Yen Chih Chen*, Jinn-Moon Yang, Chi Hung Tsai, Cheng Yan Kao

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)125-135
Number of pages11
JournalLecture Notes in Computer Science
Volume3449
DOIs
StatePublished - 19 Sep 2005
EventEvoWorkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC - Lausanne, Switzerland
Duration: 30 Mar 20051 Apr 2005

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