title: Improving the predicting power of partial order based QSARs through linear extensions
abstract: Partial order theory (POT) is an attractive and operationally simple method that allows ordering of compounds, based on selected structural and/or electronic descriptors (modeled order), or based on their end points, e.g., solubility (experimental order). If the modeled order resembles the experimental order, compounds that are not experimentally investigated can be assigned a position in the model that eventually might lead to a prediction of an end-point value. However, in the application of POT in quantitative structure-activity relationship modeling, only the compounds directly comparable to the noninvestigated compounds are applied. To explore the possibilities of improving the methodology, the theory is extended by application of the so-called linear extensions of the model order. The study show that partial ordering combined with linear extensions appears as a promising tool providing probability distribution curves in the range of possible end-point values for compounds not being experimentally investigated
keyphrases: linear extensions partial order theory electronic descriptors modeled order end points solubilities quantitative structure-activity relationships predicting power improvement structural descriptors graphical representation combinatorial rule most probable linear order partially ordered set Hasse diagram organic compounds