  We applied machine learning methods to predict chemical hazards focusing on
fish acute toxicity across taxa. We analyzed the relevance of taxonomy and
experimental setup, showing that taking them into account can lead to
considerable improvements in the classification performance. We quantified the
gain obtained throught the introduction of taxonomic and experimental
information, compared to classification based on chemical information alone. We
used our approach with standard machine learning models (K-nearest neighbors,
random forests and deep neural networks), as well as the recently proposed
Read-Across Structure Activity Relationship (RASAR) models, which were very
successful in predicting chemical hazards to mammals based on chemical
similarity. We were able to obtain accuracies of over 93% on datasets where,
due to noise in the data, the maximum achievable accuracy was expected to be
below 96%. The best performances were obtained by random forests and RASAR
models. We analyzed metrics to compare our results with animal test
reproducibility, and despite most of our models "outperform animal test
reproducibility" as measured through recently proposed metrics, we showed that
the comparison between machine learning performance and animal test
reproducibility should be addressed with particular care. While we focused on
fish mortality, our approach, provided that the right data is available, is
valid for any combination of chemicals, effects and taxa.
