Antimicrobial peptides (AMPs) have gained significant attention in the field of drug discovery due to their potential therapeutic applications in the fight against antimicrobial resistance. 1-3 However, the vast number of possible peptide sequences and their complex structure-activity relationship landscape mean that it is difficult to rationally design peptides with the desired biological activity, in particular tuning their activity versus toxicity to human cells, which is often measured as hemolysis of human red blood cells. To address this issue, several machine-learning models have been developed for the de novo design of antimicrobial peptides. Because property prediction from a peptide sequence can be framed as a natural language processing problem, many of these models use architectures specifically designed for language processing tasks. Furthermore, the emergence of large language models (LLMs), such as OpenAI's GPT models, has opened new possibilities for leveraging powerful language processing capabilities in drug discovery applications. Recent attempts by Jablonka et al. to explore the capabilities of GPT-3 for predicting properties of small molecules in various applications have shown that GPT-3 was able to perform comparably or even outperform conventional statistical models, particularly in the low data regime. There also have been successful efforts into augmenting LLM capabilities to tackle tasks related to small molecule chemistry in the areas of organic synthesis, drug discovery, and materials design. Hereby, the models mainly orchestrate a set of tools to solve chemistry tasks starting from a natural language prompt. However, to the best of our knowledge LLMs have not been implemented to predict the bioactivity of peptides yet.