All models were trained using the AdamW optimizer with binary cross-entropy loss and a batch size of 32. Model performance was evaluated using several metrics: Matthew's correlation coefficient (MCC), precision, recall, area under the receiver operating characteristic curve (AUROC), and top-2 success rate (TOP-2). More information on these metrics is provided in the Supporting Information, section S.6. Hyperparameter optimization was performed through 10-fold cross-validation using the Bayesian optimization tool Optuna , with the average performance across all folds used to assess each configuration. The initial learning rate was reduced by a factor of 10 after 10 epochs without improvement, and early stopping was triggered if the validation loss did not decrease for more than 20 epochs. A total of 20 Optuna trials were conducted for initial optimization, followed by an additional 30 trials for fine-tuning the most promising model. All hyperparameters and crossvalidation performances are detailed in the Supporting Information, section S.5. The final ensemble model was constructed by retraining the model 50 times with different random initializations. Each run used the same 10% subset of the training data as a validation set for early stopping.