title: Naïve Bayes classification using 2D pharmacophore feature triplet vectors.
abstract: A naïve Bayes classifier, employed in conjunction with 2D pharmacophore feature triplet vectors describing the molecules, is presented and validated. Molecules are described using a vector where each element in the vector contains the number of times a particular triplet of atom-based features separated by a set of topological distances occurs. Using the feature triplet vectors it is possible to generate naïve Bayes classifiers that predict whether molecules are likely to be active against a given target (or family of targets). Two retrospective validation experiments were performed using a range of actives from WOMBAT, the Prous Integrity database, and the Arena screening library. The performance of the classifiers was evaluated using enrichment curves, enrichment factors, and the BEDROC metric. The classifiers were found to give significant enrichments for the various test sets.
authors.affiliation: Arena Pharmaceuticals, 6166 Nancy Ridge Drive, San Diego, California 92121, USA. pwatson@arenapharm.com
authors.forename: Paul
authors.initials: P
authors.lastname: Watson
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D010450:Endopeptidases / Q000145:classification / Q000378:metabolism; D010770:Phosphotransferases / Q000145:classification / Q000378:metabolism; D043562:Receptors, G-Protein-Coupled / Q000145:classification / Q000378:metabolism; D015203:Reproducibility of Results
chemical_list: D043562:Receptors, G-Protein-Coupled; D010770:Phosphotransferases; D010450:Endopeptidases