Institute for Physics, Albert-Ludwigs-Universität Freiburg Freiburg, Germany.
Front Pharmacol. 2013 Apr 9;4:38. doi: 10.3389/fphar.2013.00038. eCollection 2013.
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.
lazar(懒惰的结构-活性关系)是一个用于预测毒理学的模块化框架。类似于毒理学风险评估中的读通程序,lazar 为每个要预测的化合物创建局部 QSAR(定量结构-活性关系)模型。模型开发人员可以在各种算法之间进行选择,用于描述符计算和选择、化学相似性指数和模型构建。本文介绍了 lazar 框架的高级描述,并讨论了示例分类和回归模型的性能。