Prediction of corneal permeability using artificial neural networks
The purpose of this study was to develop a simple model for prediction of corneal permeability of structurally different drugs as a function of calculated molecular descriptors using artificial neural networks. A set of 45 compounds with experimentally derived values of corneal permeability
(log C) was used to develop, test and validate a predictive model. Each compound was encoded with 1194 calculated molecular structure descriptors. A genetic algorithm was used to select a subset of descriptors that best describe corneal permeability coefficient log C and a supervised network
with radial basis transfer function (RBF) was used to correlate calculated molecular descriptors with experimentally derived measures of corneal permeability. The best model, with 4 input descriptors and 12 hidden neurones was chosen, and the significance of the selected descriptors to corneal
permeability was examined. Strong correlation of predicted with experimentally derived log C values (correlation coefficient greater than 0.87 and 0.83 respectively) was obtained for the training and testing data sets. The developed model could be useful for the rapid prediction of the corneal
permeability of candidate drugs based on molecular structure alone as it does not require experimentally derived data.
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Document Type: Research Article
Centre for Pharmaceutical Research, School of Pharmaceutical, Molecular and Biomedical Sciences, University of South Australia, Australia
School of Pharmacy, The University of Auckland, Private Bag 92019, Auckland, New Zealand, Email: [email protected]
October 1, 2003
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Pharmazie is a leading journal in the field of pharmaceutical sciences. As a peer-reviewed scientific journal, Pharmazie is regularly indexed in the relevant databases like Web of science, Journal Citation Reports and many others. The journal is open for submissions from the whole spectrum of pharnaceutical sciences including Pharmaceutical Chemistry, Experimental and Clinical Pharmacology, Drug Analysis, Pharmaceutics, Pharmaceutical Biology, Clinical Pharmacy etc.
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