Programs that predict mutagenicity of chemical compouds and their integration

What is it about?

Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a large set of about 24000 data. We then built ntegrated models using probabilistic approaches, decision theory, machine learning, and voting strategies.

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Photo by Terry Vlisidis on Unsplash

Photo by Terry Vlisidis on Unsplash

Why is it important?

This paper compares different models using the same dataset. Moreover, the paper proposes different ways to combine their results to improve the prediction accuracy.