title: lazar: a modular predictive toxicology framework.
abstract: lazar (lazy structure-activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure-activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models.
authors.affiliation: Institute for Physics, Albert-Ludwigs-Universität Freiburg Freiburg, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Maunz
authors.forename: Martin
authors.initials: M
authors.lastname: Gütlein
authors.forename: Micha
authors.initials: M
authors.lastname: Rautenberg
authors.forename: David
authors.initials: D
authors.lastname: Vorgrimmler
authors.forename: Denis
authors.initials: D
authors.lastname: Gebele
authors.forename: Christoph
authors.initials: C
authors.lastname: Helma
keywords: QSAR; in silico; predictive toxicology; read across; semantic web