The model, trained in this study, employed temperature sampling for SMILES encoding. At lower temperature values, token generation is primarily based on estimated probabilities. However, as the temperature value increases, token generation tends towards uniform probability selection. The effectiveness of generating new molecules decreases with higher temperature values due to the increased randomness of token generation, leading to errors in the SMILES strings, such as missing or mismatched parentheses or ring closures. Nonetheless, higher temperature values exhibited superior performance in terms of the novelty and effectiveness of generated molecules. This is attributed to the increased randomness of token generation, which enhances molecular diversity and expands the explorable chemical space. In this experiment, the model demonstrated optimal uniqueness and novelty in generated molecules at a temperature value of 1.1 (Fig. ).