AI 中文总结
该研究针对文本到分子生成中化学验证与错误修正不足的问题，提出MolGVR框架，经ChEBI-20和PCDes实验验证可提升精确匹配性能。
AI 中文摘要
文本到分子生成通常被表述为一次性序列生成问题，模型直接将目标描述映射到分子表示。然而，分子描述常包含丰富的结构约束，违反这些约束会改变分子的身份，这使得化学验证与错误修正成为重要但未被充分探索的环节。为填补这一空白，我们提出MolGVR，一种基于化学的生成器-验证器-优化器框架：生成器推断结构证据并生成候选分子；验证器解决化学验证缺失问题，将描述转换为化学约束并据此检查候选分子；优化器解决生成失败问题，修正被验证器拒绝的候选分子。在ChEBI-20和PCDes数据集上的实验表明，MolGVR提升了精确匹配性能，结果显示将生成与可执行验证及反馈引导的优化相结合，是改进文本到分子生成的有效途径。
英文摘要
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.
Comments22 pages
