In the rapidly evolving field of drug discovery and chemical synthesis, the ability to generate novel molecules with desired properties has become a critical paradigm. Leveraging the power of transformer-based models, like Generative Pre-trained Transformers (GPT), offers a promising approach to address this challenge . Such models, once trained on a substantial dataset, can be fine-tuned to produce chemical structures that not only satisfy specific chemical criteria but also may possess desirable pharmacological attributes. In the burgeoning field of molecular design, the application of machine learning (ML) techniques has revolutionized the way scientists generate and optimize novel chemical entities. Various ML architectures, including RNNbased , LSTM-based , Transformer-based , Variational Autoencoders (VAE) , and Generative Adversarial Networks (GANs) , along with specialized models like conditional Generative Pre-trained Transformers (GPT), offer unique capabilities and challenges in the generation of molecular structures.