Despite their advantages, in silico techniques face several challenges. One major issue is the accuracy of predictions, which heavily depends on the quality of the data and models used. Incomplete or biased data can lead to unreliable results. Moreover, these techniques often require extensive computational resources and expertise in both toxicology and computational modeling. There's also the challenge of bridging the gap between laboratory findings and real-world scenarios.
