tx9b00259_si_002.zip (7.05 MB)
Hierarchical Quantitative Structure–Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity
dataset
posted on 2020-02-04, 22:29 authored by Xinhao Li, Nicole C. Kleinstreuer, Denis FourchesReliable 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.