Hierarchical Quantitative Structure-Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity

Reliable in silico approaches to replace animal testing for the evaluation of potential acute toxic effects are highly demanded by regulatory agencies. In particular, quantitative structure activity relationship (QSAR) models have been used to rapidly assess chemical induced toxicity using either continuous (regression) or discrete (classification) predictions. However, it is often unclear how those different types of models can complement and potentially help each other to afford the best prediction accuracy for a given chemical. This paper presents a novel, dual-layer hierarchical modeling method to fully integrate regression and classification QSAR models for assessing rat acute oral systemic toxicity, with respect to regulatory classifications of concern. The first layer of independent regression, binary, and multiclass models (base models) were solely built using computed chemical descriptors/fingerprints. Then, a second layer of models (hierarchical models) were built by stacking all the cross-validated out-of-fold predictions from the base models. All models were validated using an external test set, and we found that the hierarchical models did outperform the base models for all three end points. The hierarchical quantitative structure activity relationship (H-QSAR) modeling method represents a promising approach for chemical toxicity prediction and more generally for stacking and blending individual QSAR models into more predictive ensemble models.