让生成分子更准确：用化学规则实时验证并修正结果。
MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation
- 生成-验证-修正三阶段框架，结合化学约束检查分子结构
- 在ChEBI-20和PCDes数据集上精确匹配率提升明显
- 适合需要高精度分子生成的药物研发人员
文本到分子生成通常被当作一次性序列生成任务，直接将描述映射为分子表示。然而，分子描述常包含重要结构约束，违反这些约束会改变分子身份。因此，化学验证与错误修正至关重要但研究不足。为此，我们提出MolGVR——一种基于化学知识的生成-验证-精炼框架。生成器推断结构证据并生成候选分子；验证器通过将描述转化为化学约束，检查候选分子是否符合；精炼器则对验证失败的候选进行修正。在ChEBI-20和PCDes数据集上的实验表明，MolGVR显著提升了精确匹配性能。结果表明，将生成与可执行验证及反馈驱动的精炼相结合，是提升文本到分子生成效果的有效路径。
原文摘要 · Abstract (English)
Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecular representations. However, molecular descriptions often contain informative structural constraints, and violating such constraints can change the molecular identity. This makes chemical verification and error correction important but underexplored. To fill this gap, we propose MolGVR, a chemistry-grounded Generator--Verifier--Refiner framework. The Generator infers structural evidence and generates candidate molecules. The Verifier addresses the lack of chemical validation by converting descriptions into chemical constraints and checking candidates against them. The Refiner addresses generation failures by revising candidates rejected by the Verifier. Experiments on ChEBI-20 and PCDes show that MolGVR improves exact-match performance. These results suggest that coupling generation with executable verification and feedback-guided refinement is an effective way to improve text-to-molecule generation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。