The median validity ratio for most prompts remains at more than 0.9, indicating a high rate of chemically valid and unique molecules different from the parent molecules, as shown in Figure . This stands in contrast to generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which have reported validity ratios as low as 0.25 for the best-performing models. Furthermore, generative models are susceptible to mode collapse, resulting in only 2% uniquemolecules. Thus, this LLM-based approach, which is itself a transformer-based architecture, consistently generated more valid and unique molecular structures even without fine-tuning. It can serve as a potential alternative to other existing transformer-based molecular generation frameworks, such as transformer architectures in conjunction with graph neural networks (GNNs), conditional-based transformer architectures, and others. While it may seem counterintuitive, the fine prompts exhibit a lower validity ratio because they ask the model to produce similar molecules by making only small, localized changes. In contrast, the coarse prompts instruct the model to propose completely different molecules so the model can select molecules that are somewhat unrelated to the parent but are known to be valid molecules. The LLM's occasional failure to generate valid and unique molecules may be attributed to the discrete SMILES representation, which allows many ways for the model to construct invalid strings.