To overcome this difficulty, we developed an optimization framework called DyRAMO (Dynamic Reliability Adjustment for Multi-objective Optimization), which performs multi-objective optimization while maintaining the reliability of multiple prediction models. DyRAMO was designed to adjust the appropriate reliability level for each objective, achieving a balance between high prediction reliability and predicted properties of the designed molecules. In DyRAMO, the reliability levels were explored through repeated molecular designs. To make this exploration efficient, Bayesian optimization (BO) was integrated into this approach. To demonstrate the efficacy of DyRAMO, we designed epidermal growth factor receptor (EGFR) inhibitors while maintaining high reliability for three properties: inhibitory activity against EGFR, metabolic stability, and membrane permeability. Appropriate reliability levels were efficiently explored using BO, and promising molecules, including known inhibitors, were successfully designed. In addition, we face a situation in which the reliability need to be varied for each property in a practical multi-objective optimization. We reported that such a prioritization is also possible by adjusting the DyRAMO settings. These results demonstrate the effectiveness of the data-driven generative strategy using DyRAMO. The DyRAMO is available at .