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Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices

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Laboratoire de Neuro-Heuristique, Institut de Physiologie, Rue du Bugnon 7, Lausanne, CH-1005, Switzerland, and Biomedical Department, Institute of Bioorganic & Petroleum Chemistry, Murmanskaya 1, Kiev-660, 253660, Ukraine
Cite this: J. Chem. Inf. Comput. Sci. 2001, 41, 6, 1488–1493
Publication Date (Web):September 19, 2001
https://doi.org/10.1021/ci000392t
Copyright © 2001 American Chemical Society
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Abstract

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r2 = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/∼itetko/logp.

*

 Corresponding author phone: ++41-21-692.5534; fax:  ++41-21-692.5505; e-mail:  [email protected]

 Institut de Physiologie.

 Institute of Bioorganic & Petroleum Chemistry.

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