Historically, computational toxicity prediction relied on statistical methods, structural alerts, and quantitative structureactivity relationship (QSAR) models , often based on chemical descriptors or fingerprint representations. However, a recent surge in popularity revolves around natural language processing (NLP) used for representation learning in molecular modeling, exemplified by the high accuracy of the transformer CNN for Ames mutagenicity prediction . The Ames test serves as a screen for mutagenic compounds, and had important advantages when it was first introduced, including less test chemical, time, plastic and can be automated . In NLP-based molecular representation for prediction of Ames, Simplified Molecular Input Line Entry System (SMILES) are used within an NLP-driven machine learning (ML) model, such as sequence-to-sequence (seq2seq) . Later state-of-the-art NLP models used augmentation strategies including masking , which also spread to NLP models for cheminformatics . The high accuracy was partly achieved with data augmentation, that optimize internal representations by introducing multiple iterations of SMILES strings in an auto-translation task to capture intricate structural nuances .