The accuracy of predictive models in toxicology varies depending on the complexity of the models and the availability of high-quality data. While some models, like QSAR, can offer reliable predictions for certain classes of chemicals, others may require more comprehensive datasets to improve accuracy. Continuous advancements in computational toxicology and machine learning are enhancing the precision and robustness of these predictive tools.
