Model validation is a critical step in ensuring the reliability of QSAR predictions. Several validation techniques are commonly used: 1. Internal Validation: Techniques like cross-validation and bootstrapping are used to assess the model's performance on the training dataset. 2. External Validation: The model is tested on an independent dataset that was not used during the model-building process to evaluate its predictive power. 3. Statistical Metrics: Metrics such as R², RMSE, and Q² are used to quantify the model's accuracy and robustness.
