Prediction of the effect of formulation on the toxicity of chemicals
Pritesh Mistry, Daniel C. Neagu, Antonio Sánchez‐Ruiz, Paul R. Trundle, Jonathan D. Vessey, John Paul Gosling · Toxicology Research · 2016
Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.