Language of Toxicity: An eXplainable Artificial Intelligence Approach

Toxic and nontoxic chemicals, represented by canonical SMILES, are investigated as the words of two languages which have to be discriminated; using eight different end points, the model provided an accurate description of toxicity patterns, with an average Area under the ROC curve (AUC) of 0.83 (min: 0.70, max: 0.94) under repeated cross-validation. The models were trained on relatively small data sets (∼1000 samples) and often strongly imbalanced, two important challenges that highlight the opportunities for future improvement; moreover, the proposed attention-based framework offers a representation of the molecular regions influencing model predictions, providing a basis for future investigations into toxicity-related structural patterns and potentially supporting hypothesis generation in drug design or drug repurposing applications.