J Chem Inf Model - Advances in the replacement and enhanced replacement method in QSAR and QSPR theories.

Tópicos

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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

The selection of an optimal set of molecular descriptors from a much greater pool of such regression variables is a crucial step in the development of QSAR and QSPR models. The aim of this work is to further improve this important selection process. For this reason three different alternatives for the initial steps of our recently developed enhanced replacement method (ERM) and replacement method (RM) are proposed. These approaches had previously proven to yield near optimal results with a much smaller number of linear regressions than the full search. The algorithms were tested on four different experimental data sets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that one of the new alternatives further improves the ERM, which has shown to be superior to genetic algorithms for the selection of an optimal set of molecular descriptors from a much greater pool. The new proposed alternative also improves the simpler and the lower computational demand algorithm RM.

Resumo Limpo

select optim set molecular descriptor much greater pool regress variabl crucial step develop qsar qspr model aim work improv import select process reason three differ altern initi step recent develop enhanc replac method erm replac method rm propos approach previous proven yield near optim result much smaller number linear regress full search algorithm test four differ experiment data set form collect experiment record differ compound molecular descriptor respect comparison show one new altern improv erm shown superior genet algorithm select optim set molecular descriptor much greater pool new propos altern also improv simpler lower comput demand algorithm rm

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