A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors
- PMID: 15012980
- DOI: 10.1016/j.bmcl.2003.12.024
A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors
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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