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A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors
Citation:
Carriger, J., T. Martin, AND M. Barron. A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, 180:11-24, (2016).
Impact/Purpose:
This manuscript reports on the development of a Bayesian network prediction model for classifying aquatic toxicity mode of action (MOA). The impact of this work is that it is the first network classification model for MOA and provides a computational chemistry based tool for MOA prediction. This represents an innovation, as it gives users a new tool to predict MOA, which is used to group chemicals for multi-chemical analyses.
Description:
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by identifying a subset of the key chemical descriptors associated with broad aquatic toxicity MoAs, and by providing a computational chemistry-based network classification model with reasonable prediction accuracy.
