1. Reliance on the quality and availability of data: ChatGPT is only as good as the data it has been trained on. If the data is incomplete, biased, or inaccurate, the model's predictions may not be reliable. 2. Lack of experimental validation: ChatGPT can generate predictions and hypotheses, but it cannot perform experiments or measure the properties of compounds. Therefore, the predictions made by the model need to be validated experimentally. 3. Limited understanding of the underlying biology: While ChatGPT can generate humanlike text, it does not understand the underlying biology of the systems it is simulating. Therefore, the predictions made by the model may not always reflect the true complexity of the systems. 4. Limited interpretability: ChatGPT, like other machine learning models, can be difficult to interpret, and it's not always clear how the model arrived at a particular prediction. 5. Limitations in handling uncertainty: ChatGPT is a deterministic model, it cannot account for the uncertainty in the data and predictions. 6. Lack of transparency: ChatGPT is a black box model, it is difficult to understand and explain the internal workings of the model, which can make it difficult to trust the model's predictions.