To test model robustness we employed three data splitting strategies: a random split for maximum data variance exposure, a kinasebased split to eliminate kinase leakage, and a family-based split to prevent family bias in training data. Models were built using XG-Boost, optimized with hyperparameter tuning via Optuna 47 (See SI for details). For each experimental condition, the optimal set of parameters was identified and used to train the best model using internal cross-validation splits appropriate to the chosen data splitting strategy.