DyMol: Dynamic Many-Objective Molecular Optimization with Objective Decomposition and Progressive Optimization
Track: Machine learning: computational method and/or computational results
Cell: I do not want my work to be considered for Cell Systems
Keywords: Molecular optimization, dynamic many-objective, drug discovery
TL;DR: novel method designed to tackle the dynamic many-objective molecular optimization problem by utilizing objective decomposition strategy
Abstract: 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.
Submission Number: 9
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