这篇论文把分子生成从一把梭变成边生成边检查，验证不过就改，效果在 ChEBI-20 和 PCDes 上有提升，做化学AI的值得看看。
MolGVR 提出生成器-验证器-精炼器三段式框架，用于解决文本到分子生成中的化学约束违反问题。生成器先推断结构证据并产出候选分子，验证器将描述转化为化学约束逐一检查，精炼器则对未通过验证的候选进行修正。在 ChEBI-20 和 PCDes 基准上，MolGVR 提升了精确匹配性能。研究显示，将生成与可执行验证及反馈修正结合，能有效改善文本到分子生成任务。
MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation
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.