QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

ABSTRACT: The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. T

SUBMITTER: Basant N 

PROVIDER: S-EPMC6062388 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature