A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas)

Abstract

REACH regulation demands information about acute toxicity of chemicals towards fish and supports the use of QSAR models, provided compliance with OECD principles. Existing models present some drawbacks that may limit their regulatory application. In this study, a dataset of 908 chemicals was used to develop a QSAR model to predict the LC₅₀ 96 hours for the fathead minnow. Genetic algorithms combined with k nearest neighbour method were applied on the training set (726 chemicals) and resulted in a model based on six molecular descriptors. An automated assessment of the applicability domain (AD) was carried out by comparing the average distance of each molecule from the nearest neighbours with a fixed threshold. The model had good and balanced performance in internal and external validation (182 test molecules), at the expense of a percentage of molecules outside the AD. Principal Component Analysis showed apparent correlations between model descriptors and toxicity.

Keywords:

• QSAR
• fathead minnow
• aquatic toxicity
• REACH
• similarity
• kNN

View correction statement:

Erratum

Acknowledgements

The authors wish to acknowledge Eva Bay Wedebye and Nikolai Georgiev Nikolov from the Technical University of Denmark, National Food Institute for helping with the curation of the dataset and making software packages and the OpenTox database available.