Molecular discovery has received significant attention across various scientific fields by enabling the creation of novel chemical compounds. In recent years, the majority of studies have approached this process as a multi-objective optimization problem. Despite notable advancements, most methods optimize only up to four molecular objectives and are mainly designed for scenarios with a predetermined number of objectives. However, in real-world applications, the number of molecular objectives can be more than four (many-objective) and additional objectives may be introduced over time (dynamic-objective). To fill this gap, we propose DyMol, the first method designed to tackle the dynamic many-objective molecular optimization problem by utilizing a novel divide-and-conquer approach combined with a decomposition strategy. We validate the superior performance of our method using the practical molecular optimization (PMO) benchmark.