Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction

We have implemented a naive Bayesian classifier which models continuous numerical data using a Gaussian distribution. Several cases of interest in the area of absorption, distribution, metabolism, and excretion prediction are presented which demonstrate that this approach is superior to the implementation of naive Bayesian classifiers in which continuous chemical descriptors are modeled as binary data. We demonstrate that this enhanced performance, upon comparison with other implementations, is independent of the descriptor sets chosen. We also compare the performance of three implementations of naive Bayesian classifiers with other previously described models.