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. 2004 Feb 23;14(4):851-3.
doi: 10.1016/j.bmcl.2003.12.024.

A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors

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A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors

László Molnár et al. Bioorg Med Chem Lett. .

Abstract

An artificial neural network based approach using Atomic5 fragmental descriptors has been developed to predict the octanol-water partition coefficient (logP). We used a pre-selected set of organic molecules from PHYSPROP database as training and test sets for a feedforward neural network. Results demonstrate the superiority of our non-linear model over the traditional linear method.

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