Prediction of mechanisms of toxic action of phenols by means of probabilistic neural network in combination with Kruskal-Wallis test

Abstract

Prediction of the toxicity of chemical compounds is one of the most important steps in drug design. The use of phenolic compounds is a promising component in the pharmaceutical industry with many possible applications. The paper focuses on the application of a probabilistic neural network for classifying 232 phenols based on their mechanisms of toxic action. The Kruskal–Wallis test was also used to assess the influence of molecular descriptors on the reliable classification of phenolic compounds based on the mechanisms of their toxic action. It is shown that for the correct training of a probabilistic neural network and effective prediction of the mechanisms of toxic action of phenols, it is sufficient to use only 5 molecular descriptors.