title: Benchmarking of QSAR models for blood-brain barrier permeation.
abstract: Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.
authors.affiliation: School of Mathematics, Physics and Information Technology, James Cook University, Townsville, Australia. dmitry.konovalov@jcu.edu.au
authors.forename: Dmitry A
authors.initials: DA
authors.lastname: Konovalov
authors.forename: Danny
authors.initials: D
authors.lastname: Coomans
authors.forename: Eric
authors.initials: E
authors.lastname: Deconinck
authors.forename: Yvan Vander
authors.initials: YV
authors.lastname: Heyden
mesh_terms: D001812:Blood-Brain Barrier; D008958:Models, Molecular; D009010:Monte Carlo Method; D021281:Quantitative Structure-Activity Relationship