Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction

Owing to the high cost and time associated with developing and validating anti-cancer drugs in clinical trials which is further exacerbated by the 96% failure rate, the development of preclinical computational models that can accurately predict whether a cell line is sensitive or resistant to a particular drug is imperative. The availability of large-scale pharmacogenomics datasets collected via high-throughput screening technologies offers feasible resources to develop robust drug response models and identify the important biomarkers predictive of drug sensitivity. Large language models (LLM), such as the Generative Pre-trained Transformer (GPT-3) from OpenAI, are "taskagnostic models" pre-trained on large textual corpora crawled from the Web that have exhibited unprecedented capabilities on a broad array of NLP tasks.