Prediction of partition coefficient based on atom-type electrotopological state indices
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Updated: Oct 25, 2026

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Published on: June 28, 2018
J J Huuskonen1, A E Villa, I V Tetko
1Division of Pharmaceutical Chemistry, Department of Pharmacy, POB 56, FIN-00014 University of Helsinki, Finland.
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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