Dropout, a technique to combat overfitting by deactivating a subset of neurons during training, slows down the training rate as the dropout value increases. After altering the dropout value, the overall model showed no significant difference in molecular generation outcomes, leading to the selection of an intermediate value of 0.3 (Fig. ).