Dynamic Many-Objective Molecular Optimization: Unfolding Complexity with Objective Decomposition and Progressive Optimization

摘要

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. Additionally, we comprehensively integrate convergence, Pareto diversity, and structural diversity into the optimization process to provide efficient exploration of the search space. We validate the superior performance of our method using the practical molecular optimization (PMO) benchmark. The source code and supplementary material are available online.