A dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines. The proposed model is capable to explain the hepatocarcinogenic toxicity and could be exploited for predicting this property for chemicals at the early stage of their development, so optimizing resources and reducing animal testing.
Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals
IMBRIANI, MARCELLO;
2009-01-01
Abstract
A dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines. The proposed model is capable to explain the hepatocarcinogenic toxicity and could be exploited for predicting this property for chemicals at the early stage of their development, so optimizing resources and reducing animal testing.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
