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Modeling the properties of chemical mixtures is a difficult but importantpart of any modeling process intended to be applicable to the often messy andimpure phenomena of everyday life, including food and environmental safety,healthcare, etc. Part of this difficulty stems from the increased complexity ofdesigning suitable model validation schemes for mixture data, a fact which hasbeen elucidated in previous work only in the case of binary mixture models. Weextend these previously defined validation strategies for QSAR modeling ofbinary mixtures to the more complex case of general, $N$-ary mixtures and arguethat these strategies are applicable to many modeling tasks beyond simplechemical mixtures. Additionally, we propose a method of establishing a baselinemodel performance for each mixture dataset to be in used in model selectioncomparisons. This baseline is intended to account for the statisticaldependence generically present between the properties of mixtures that shareconstituents. We contend that without such a baseline, estimates of modelperformance can be dramatically overestimated, and we demonstrate this withmultiple case studies using real and simulated data.

The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation