title: Modeling promiscuity based on in vitro safety pharmacology profiling data.
abstract: This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.
authors.affiliation: CPC/LFP/MLI, Novartis Institutes for Biomedical Research, Novartis Pharma AG, Postfach, 4002 Basel, Switzerland. kamal.azzaoui@novartis.com
authors.forename: Kamal
authors.initials: K
authors.lastname: Azzaoui
authors.forename: Jacques
authors.initials: J
authors.lastname: Hamon
authors.forename: Bernard
authors.initials: B
authors.lastname: Faller
authors.forename: Steven
authors.initials: S
authors.lastname: Whitebread
authors.forename: Edgar
authors.initials: E
authors.lastname: Jacoby
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Jenkins
authors.forename: Laszlo
authors.initials: L
authors.lastname: Urban
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000639:trends; D016503:Drug Delivery Systems; D015195:Drug Design; D064420:Drug-Related Side Effects and Adverse Reactions; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D010600:Pharmacology / Q000379:methods* / Q000639:trends*; D013329:Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations