Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation
- 21 December 2002
- journal article
- research article
- Published by American Chemical Society (ACS) in Journal of Chemical Information and Computer Sciences
- Vol. 43 (2) , 429-434
- https://doi.org/10.1021/ci025590u
Abstract
Two quantitative models for the prediction of aqueous solubility of 1293 organic compounds were developed by a Multilinear Regression (MLR) analysis and a Back-Propagation (BPG) neural network. The molecules were described by a set of 32 values of a Radial Distribution Function (RDF) code representing the 3D structure and eight additional descriptors. The 1293 compounds were divided into a training set of 797 compounds and a test set of 496 compounds based on a Kohonen self-organizing neural network map. The obtained models show a good predictive power: for the test set, a correlation coefficient of 0.96 and a standard deviation of 0.59 were achieved by the back-propagation neural network approach.Keywords
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