Molecule optimization is a critical task in drug discovery to optimize desired properties of a given molecule through chemical modification. Despite Large Language Models (LLMs) holding the potential to efficiently simulate this task by using natural language to direct the optimization, straightforwardly utilizing shows limited performance. In this work, we facilitate utilizing LLMs in an iterative paradigm by proposing a simple yet highly effective domain feedback provider, namely $\text{Re}^3$DF. In detail, $\text{Re}^3$DF harnesses an external toolkit, RDKit, to handle the molecule hallucination, if the modified molecule is chemically invalid. Otherwise, its desired properties are computed and compared to the original one, establishing reliable domain feedback with correct direction and distance towards the objective, followed by a retrieved example, to explicitly guide the LLM to refine the modified molecule. We conduct experiments across both single- and multi-property objectives with 2 thresholds, where $\text{Re}^3$DF shows significant improvements. Particularly, for 20 single-property objectives, $\text{Re}^3$DF enhances Hit ratio by 16.95% and 20.76% under loose and strict thresholds, respectively. For 32 multi-property objectives, $\text{Re}^3$DF enhances Hit ratio by 6.04% and 5.25%.

翻译：分子优化是药物发现中的关键任务，旨在通过化学修饰优化给定分子的目标性质。尽管大语言模型（LLMs）具备通过自然语言指导优化过程以高效模拟该任务的潜力，但直接使用LLMs的表现有限。本研究通过提出一种简单而高效的领域反馈提供器——$\text{Re}^3$DF，促进LLMs在迭代范式中的应用。具体而言，$\text{Re}^3$DF利用外部工具包RDKit处理分子幻觉问题，即当修饰后的分子化学结构无效时进行校正。若分子有效，则计算其目标性质并与原始分子进行比较，建立具有正确优化方向和距离的可靠领域反馈，随后结合检索到的示例，显式指导LLM对修饰分子进行细化改进。我们在单目标与多目标优化任务中设置了两种阈值进行实验，结果表明$\text{Re}^3$DF均带来显著性能提升。特别地，在20个单目标优化任务中，$\text{Re}^3$DF在宽松与严格阈值下分别将命中率提升了16.95%和20.76%；在32个多目标优化任务中，命中率分别提升了6.04%和5.25%。