In the molecular design examined in Section 2.2, several molecules, including gefitinib, spanned multiple training datasets, leading to a situation in which the overlap of the ADs for each property was relatively large. Therefore, molecules likely to span multiple ADs were removed from the training data of the prediction model. Specifically, for data with MTS greater than 0.5, across the three datasets, a total of 777 molecules were removed (Fig. ). The prediction models were reconstructed using the partially removed dataset, and the reliability levels were explored using DyRAMO under the same conditions described in Section 2.2. ChemTSv2 with adjusted reliability levels successfully reproduced known EGFR inhibitors that were excluded from the training dataset (Fig. ). These molecules have high experimental pIC 50 values against EGFR (approximately 7-8), with generally optimized predicted metabolic stability and membrane permeability. This suggests that DyRAMO has the potential to identify new drug candidates even when the molecules in each training dataset are not similar. Remove 777 molecules with a maximum Tanimoto similarity greater than 0.5 across the three datasets.