Despite their benefits, predictive models in toxicology face several challenges:
Data Quality: The accuracy of predictions heavily depends on the quality and comprehensiveness of the data used to train models. Validation: Ensuring that models are reliable and applicable to real-world scenarios is crucial, which requires extensive validation and cross-verification with experimental data. Complexity: The biological systems are inherently complex, and capturing this complexity in computational models is challenging. Interdisciplinary Approach: Effective predictive modeling requires collaboration among experts in fields such as chemistry, biology, and computer science, which can be difficult to coordinate.
