title: Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction.
abstract: We have implemented a naïve 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 naïve 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 naïve Bayesian classifiers with other previously described models.
authors.affiliation: Department of Molecular Modeling, Pharmacopeia Drug Discovery, Inc., P.O. Box 5350, Princeton, New Jersey 08543-5350, USA. aklon@pcop.com
authors.forename: Anthony E
authors.initials: AE
authors.lastname: Klon
authors.forename: Jeffrey F
authors.initials: JF
authors.lastname: Lowrie
authors.forename: David J
authors.initials: DJ
authors.lastname: Diller
mesh_terms: D001499:Bayes Theorem; D001798:Blood Proteins / Q000378:metabolism; D001812:Blood-Brain Barrier; D006801:Humans; D007408:Intestinal Absorption; D010599:Pharmacokinetics; D012372:ROC Curve
chemical_list: D001798:Blood Proteins