While in silico testing offers significant benefits, it also has limitations:
Model accuracy: The reliability of predictions depends on the quality and quantity of data used to train the models. Complex biological processes: Some biological interactions and pathways are too complex to be accurately modeled, limiting predictive capabilities. Data availability: The lack of comprehensive data sets for certain substances or mechanisms can hinder model development and validation.
