In this paper, a quantitative structure-retention relationship (QSRR) method is employed to model the retention behaviour in reversed-phase high-performance liquid chromatography of arylpropionic acid derivatives, largely used non-steroidal anti-inflammatory drugs (NSAIDs). Computed molecular descriptors and the organic modifier content in the mobile phase are associated into a comprehensive model to describe the effect of both solute structure and eluent composition on the isocratic retention of these drugs in water-acetonitrile mobile phases. Multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is used to extract from a large set of computed 3D descriptors an optimal subset. Based on GA-MLR analysis, a five-dimensional QSRR model is identified. All the four selected molecular descriptors belong to the category of GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors. The related multilinear model exhibits a quite good fitting and predictive performance. This model is further improved using an artificial neural network (ANN) learned by error back-propagation. Finally, the ANN-based model displays a remarkably better performance as compared with the MLR counterpart and, based on external validation, is able to predict with good accuracy the behaviour of unknown arylpropionic NSAIDs in the range of mobile phase composition of analytical interest (between 35 and 75% acetonitrile (v/v)). (c) 2007 Elsevier B.V. All rights reserved.
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