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Paper   IPM / Nano-Sciences / 8228
School of Nano Science
  Title:   Comparison of Multiple Linear Regression and Artificial Neural Networks in Predicting Octanol/ Water Partition Coefficient of a Variety of Organic Molecules
1.  Seifollah Jalili
2.  M. Tafazzoli
3.  M. Jalali-Heravi
  Status:   Published
  Journal: Journal of Theoretical and Computational Chemistry
  No.:  3
  Vol.:  2
  Year:  2003
  Pages:   335-344
  Publisher(s):   World Scientific Publishing Company
  Supported by:  IPM
For estimating log P values of a group of organic compounds, a back-propagation neural network with a 9–6–1 architecture was developed with optimal learning rate (ε) and momentum (μ) of 0.24 and 0.82, respectively. A collection of 131 organic compounds was chosen as data set that consists of normal hydrocarbons, alcohols, ethers, amines, ketones, acids, benzene derivatives, phenols, and aldehydes. The data set was divided into a training set consisting of 118 molecules and a prediction set consisting of 18 molecules. The most important properties that affect the partition coefficients of organic compounds (surface/volume, dipole moment, and those which are related to electrostatic potentials such as the sum of charges on the carbon atoms) were used as descriptors. These descriptors were obtained using AM1 semiempirical MO method for the gas phase geometries. The descriptors were selected via developing a multiple linear regression analysis. The ANN calculated values of partition coefficients (log Ps) for molecules of the training and prediction sets are in good agreement with the experimental values.

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