title: Data set modelability by QSAR.
abstract: We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (correct classification rate above 0.7) for a binary data set of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of nearest-neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 data sets, and the threshold of 0.65 was found to separate the nonmodelable and modelable data sets.
authors.affiliation: Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
authors.forename: Alexander
authors.initials: A
authors.lastname: Golbraikh
authors.forename: Eugene
authors.initials: E
authors.lastname: Muratov
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D019295:Computational Biology; D062126:Databases, Chemical / Q000706:statistics & numerical data; D015195:Drug Design; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship