Prediction of the n-octanol/water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network
Breindl A, Beck B, Clark T, Glen R (1997)
Publication Status: Published
Publication Type: Journal article
Publication year: 1997
Journal
Publisher: Springer Verlag (Germany)
Book Volume: 3
Pages Range: 142-155
Journal Issue: 3
Abstract
A back-propagation artificial neural net has been trained to estimate logP values of a large range of organic molecules from the results of AM1 and PM3 semiempirical MO calculations. The input descriptors include molecular properties such as electrostatic potentials, total dipole moments, mean polarizabilities, surfaces, volumes and charges derived from semiempirical calculated gas phase geometries. These properties can be related to the molecule's solubility in hydrophilic or lipophilic media. The input descriptors were selected with the help of a multiple linear regression analysis. The resulting net estimates the logP values of 105 organic compounds with a standard deviation of 0.53 units from the experimental logP values for AM1 and 0.67 units in the case of PM3.
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Germany (DE)How to cite
APA:
Breindl, A., Beck, B., Clark, T., & Glen, R. (1997). Prediction of the n-octanol/water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network. Journal of Molecular Modeling, 3(3), 142-155. https://doi.org/10.1007/s008940050027
MLA:
Breindl, Andreas, et al. "Prediction of the n-octanol/water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network." Journal of Molecular Modeling 3.3 (1997): 142-155.
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