MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation

AI 中文解读

1. 核心亮点：这项研究给AI配了一位“化学老师”，让AI在生成分子结构时先检查再修改，准确率显著提升。 2. 通俗解读：过去，让AI根据文字描述生成分子，就像让一个新手直接画化学结构式，画错了也没人提醒。现在这个叫MolGVR的新框架，相当于给AI增加了“三重保险”：先根据描述画出候选分子，再像做选择题一样逐条核对描述中的化学规则，最后把不符合要求的分子打回去重新改。整个过程就像写作文时先打草稿、再检查错别字、最后修改润色，让AI交出的“分子答卷”更靠谱。 3. 实际影响：这项技术主要影响药物研发和材料科学领域。未来你吃的药、用的塑料或纤维，可能都是AI根据“想要什么功能”直接设计出来的分子。这能大幅缩短新药从论文到临床试验的周期，降低研发成本。对普通人而言，更快的药物研发意味着未来面对疑难杂症时，能有更多、更便宜的靶向药可选。此外，这项“生成-验证-修正”的思路也能启发其他AI应用，让AI生成内容时更严谨、更实用。

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.