  In this study, we investigated the potential of GPT-3 for the anti-cancer
drug sensitivity prediction task using structured pharmacogenomics data across
five tissue types and evaluated its performance with zero-shot prompting and
fine-tuning paradigms. The drug's smile representation and cell line's genomic
mutation features were predictive of the drug response. The results from this
study have the potential to pave the way for designing more efficient treatment
protocols in precision oncology.
