Multi-Objective Genetic Algorithm (MOGA) As a Feature Selecting Strategy in the Development of Ionic Liquids’ Quantitative Toxicity–Toxicity Relationship Models

Quantitative toxicity–toxicity
relationship (QTTR) models
have a great potential for improving the meaning of toxicological
tests conducted on simple organisms. These models allow predicting
the toxicological effect of a chemical based on its known toxicological
effect in different toxicity tests, even against a different organism.
This fact poses a great potential for predicting the toxicity of chemicals
against higher organisms based on the results against lower ones.
However, the possibility of developing such models is often restricted
due to the low availability of data. We present a case study of developing
the QTTR model for ionic liquids in different toxicological tests
against the same species, in the face of insufficient experimental
data (an additional confirmation for a different species is provided
in the Supporting Information). In the presented case, we use a series
of quantitative structure–activity relationship (QSAR) models
developed to deliver the data concerning the toxicity of ionic liquids
against human HeLa and MCF-7 cancer cell lines. We use these data
to develop a QTTR model with an R2 as
high as 0.8. The benefit of applying the multi-objective genetic algorithm
(MOGAa genetic algorithm allowing for selection of the best
set of explanatory features for several different dependent variables
at the same time) as a QSAR model feature selecting strategy is presented
and discussed.