In this study, compounds were represented as SMILES strings without stereochemistry. These strings were tokenized on a single-character basis, with some exceptions (e.g., "Cl", "[nH]", and "%10") treated as single tokens. To improve generalization, SMILES randomization was applied during training by shuffling atom ordering in each epoch. The chemical language models were based on the LLaMA 43 architecture, a transformer variant that employs rotary positional embeddings, the SwiGLU 45 activation function, and pre-normalization using the RMSNorm 46 normalization function.