The σ used in this study was three patterns: 0.15 (i) for tight, 0.25 (ii) for normal, and 0.35 (iii) for loose. The σ for each property here was set to (i) for inhibitory activity, and (ii) for metabolic stability and membrane permeability. Details of the development of prediction models for these properties, the molecular design using ChemTSv2, and the search for appropriate reliability levels using DyRAMO are described in the Methods section. To verify the effect of the molecular design on the prediction reliability, the same molecular design without considering the reliability was performed. Fig. shows the results of the molecular designs with reliability levels adjusted by DyRAMO (Figs. ) and without considering the prediction reliability (Figs. ). The optimized reliability levels were 0.66 for inhibitory activity, 0.55 for metabolic stability, and 0.43 for membrane permeability, and the exploration process of the reliability levels are discussed in the next paragraph, and Fig. . Fig. shows the optimization process for the predicted properties and reliability of the molecules designed using ChemTSv2 with the adjusted reliability levels. The MTS between a designed molecule and the training data for each property was evaluated as the reliability of the designed molecule. The series in Fig. and shows the moving averages of each of the 200 designed molecules. As the design progressed, the predicted values of the three properties, particularly the inhibitory activity, increased. The reliabilities of the designed molecules exceeded the set reliability levels (dotted lines) during molecular design, indicating that they were included in the adjusted AD for each model. Figs. and show examples of the designed molecules along with the predicted property values and MTSs of the training data. These molecules were predicted to have an inhibitory activity against EGFR (pIC 50 ) of over 8, with the other two properties also generally well optimized. Remarkably, several known inhibitors, including the marketed drug gefitinib, were also designed using DyRAMO, as shown in Figs. and. From the viewpoint of drug design, these molecules have a quinazoline substructure (highlighted in Figs. ), a characteristic substructure of known EGFR inhibitors , suggesting that the essential feature of EGFR inhibitors is captured during the molecular design process. However, as shown in Figs. and, multiobjective optimization without considering prediction reliability resulted in reward hacking. Although the predicted properties were optimized, the designed molecules differed significantly from the training data. The designed molecules shown in Fig. are far from known EGFR inhibitors. These results indicate that with the adjustment of the reliability levels by DyRAMO, a promising molecular design can be effectively achieved. properties were well-optimized. The shown molecules were selected according to the following procedure. Molecules that exceeded the set reliability levels were obtained from ten times molecular designs with high DSS scores. Molecules that passed two filters, the rule of five filter and the Pub-Chem filter, were extracted. The rule of five filter is a filter based on Lipinski's rule of five , and the PubChem filter is a filter based on the frequency of occurrence of molecular patterns in the PubChem database. Subsequently, k-means clustering (k=20) was conducted, and the molecules with the highest reward from each cluster were selected. Other designed examples are shown in Fig. . The highlighted areas of molecules represent the quinazoline substructure, a characteristic substructure of known EGFR inhibitors. Inhibitory activity against EGFR (pIC 50 ), metabolic stability (remaining percentage in one hour), and membrane permeability (µcm/s) are colored in blue, green, and red, respectively. Molecules with a MTS of 1.0 have their experimental values noted at the top of each table. (d) Designed molecules identical to known molecules that are active against EGFR. DyRAMO reproduced gefitinib, one of the approved drugs against EGFR (the left end of d). The other reproduced molecules are shown in Fig. . e, Examples of molecules designed without reliability consideration. The molecular designs for 10,000 molecules were conducted three times, and designed molecules that passed both the rule of five filter and the PubChem filter were extracted. K-means clustering was performed on these extracted molecules, and the molecule with the largest reward in each cluster was selected. Five molecules are shown here and the others are shown in Fig. .