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Front Pharmacol. 2013 Apr 9;4:38. doi: 10.3389/fphar.2013.00038. eCollection 2013.

lazar: a modular predictive toxicology framework.

Author information

1
Institute for Physics, Albert-Ludwigs-Universit├Ąt Freiburg Freiburg, Germany.

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.

KEYWORDS:

QSAR; in silico; predictive toxicology; read across; semantic web

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