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.