Bioavailability prediction based on molecular structure for a diverse series of drugs.

PURPOSE: Radial basis function artificial neural networks and theoretical descriptors were used to develop a quantitative structure-pharmacokinetic relationship for structurally diverse drug compounds.
METHODS: Human bioavailability values were taken from the literature and descriptors were generated from the drug structures. All models were trained with 137 compounds and tested with a further 15, after which they were evaluated for predictive ability with an additional 15 compounds.
RESULTS: The final model possessed a 10-31-1 topology and training and testing correlation coefficients were 0.736 and 0.897, respectively. Predictions for independent compounds agreed well with experimental literature values, especially for compounds that were well absorbed and/or had high observed bioavailability. Important theoretical descriptors included solubility parameters, electronic descriptors, and topological indices.
CONCLUSIONS: Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development.