Structural Similarity,Activity, and Toxicity of Mycotoxins:Combining Insights from Unsupervised and Supervised Machine LearningAlgorithms

A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity and presence of co-occurring compounds. This work addresses a fundamental question: Can we assess molecular similarity and predict the toxicity of mycotoxins <i>in silico</i> using a defined set of molecular descriptors? We propose a rapid nontarget screening approach for multiple classes of mycotoxins, integrating both unsupervised and supervised machine learning models, alongside molecular and physicochemical descriptors to enhance the understanding of structural similarity, activity, and toxicity. Clustering analyses identify natural clusters corresponding to the known mycotoxin families, indicating that mycotoxins belonging to the same cluster share similar molecular properties. However, topological descriptors play a significant role in distinguishing between acutely toxic and nonacutely toxic compounds. Random forest (RF) and neural networks (NN), combined with molecular descriptors, contribute to improved knowledge and predictive capability regarding mycotoxin toxicity profiles. RF allows the prediction of toxicity using data reflecting mainly structural features and performs well in the presence of descriptors reflecting biological activity. NN models prove to be more sensitive to biological activity descriptors than RF. The use of descriptors encompassing structural complexity and diversity, chirality and symmetry, connectivity, atomic charge, and polarizability, together with descriptors representing lipophilicity, absorption, and permeation of molecules, is crucial for predicting toxicity, facilitating broader toxicological evaluations.