Molecular editing-modifying a given molecule to improve desired properties-is a fundamental task in drug discovery. While LLMs hold the potential to solve this task using natural language to drive the editing, straightforward prompting achieves limited accuracy. In this work, we propose AgentDrug, an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy. AgentDrug defines a nested refinement loop: the inner loop uses feedback from cheminformatics toolkits to validate molecular structures, while the outer loop guides the LLM with generic feedback and a gradient-based objective to steer the molecule toward property improvement. We evaluate AgentDrug on benchmarks with both single- and multi-property editing under loose and strict thresholds. Results demonstrate significant performance gains over previous methods. With Qwen-2.5-3B, AgentDrug improves accuracy by 20.7% (loose) and 16.8% (strict) on six single-property tasks, and by 7.0% and 5.3% on eight multi-property tasks. With larger model Qwen-2.5-7B, AgentDrug further improves accuracy on 6 single-property objectives by 28.9% (loose) and 29.0% (strict), and on 8 multi-property objectives by 14.9% (loose) and 13.2% (strict).

翻译：分子编辑——通过修饰给定分子以改善其期望性质——是药物发现中的一项基础任务。虽然大语言模型（LLM）有潜力通过自然语言驱动编辑来解决此任务，但直接的提示方法取得的准确率有限。在本工作中，我们提出了AgentDrug，一种智能体工作流，它利用LLM在一个结构化的精炼过程中实现了显著更高的准确率。AgentDrug定义了一个嵌套的精炼循环：内循环利用化学信息学工具包的反馈来验证分子结构，而外循环则通过通用反馈和基于梯度的目标来引导LLM，从而将分子导向性质改善的方向。我们在宽松和严格阈值下的单性质与多性质编辑基准上评估了AgentDrug。结果表明，与先前方法相比，其性能有显著提升。使用Qwen-2.5-3B模型时，AgentDrug在六项单性质任务上的准确率分别提高了20.7%（宽松）和16.8%（严格），在八项多性质任务上分别提高了7.0%和5.3%。使用更大的模型Qwen-2.5-7B时，AgentDrug在六项单性质目标上的准确率进一步提高了28.9%（宽松）和29.0%（严格），在八项多性质目标上进一步提高了14.9%（宽松）和13.2%（严格）。