Deep Generative Models (DGMs) have been implemented in chemical research applications to improve the utility of existing AI workflows. These models possess the capability to explore a greater chemical space, yielding novel structures of desired physical, chemical, and bio activity but have lacked effective natural language processing to aid humans until recent years . More capable Large Language Models trained on chemistry and biochemistry specific datasets have increased in 2024, primarily based on Decoder-only and Encoder-decoder architectures . MOLGEN was also recently trained on over 100 million molecular SELFIES to internalize structural and grammatical insights for molecular generations . Additionally, LlaSMol used over three million samples to aid in the proficiency of selected chemistry tasks . LAMM-MIT has also developed an end-to-end de novo protein generation hybrid language model .