Abstract
The aim of this study was to determine the efficacy of atom‐type electrotopological state indices for estimation of the octanol–water partition coefficient (log P) values in a set of 345 drug compounds or related complex chemical structures. Multilinear regression analysis and artificial neural networks were used to construct models based on molecular weights and atom‐type electrotopological state indices. Both multilinear regression and artificial neural networks provide reliable log P estimations. For the same set of parameters, application of neural networks provided better prediction ability for training and test sets. The present study indicates that atom‐type electrotopological state indices offer valuable parameters for fast evaluation of octanol–water partition coefficients that can be applied to screen large databases of chemical compounds, such as combinatorial libraries.
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