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

2026-07-31

AI Summary

This work addresses the limitations of existing text-to-molecule generation methods, which often produce chemically invalid structures that contradict the input textual descriptions due to inadequate enforcement and correction of chemical constraints. To overcome this, the authors propose MolGVR, a novel framework that integrates executable chemical validation and feedback-driven refinement directly into the generation pipeline. MolGVR employs a collaborative generator–verifier–refiner architecture: the generator produces candidate molecules, the verifier parses the input text into explicit chemical constraints and assesses structural compliance, and the refiner iteratively corrects violations. This closed-loop design significantly enhances generation accuracy, achieving superior exact-match performance on the ChEBI-20 and PCDes benchmarks, thereby demonstrating the effectiveness and novelty of the proposed mechanism.

Abstract

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.

Problem

• text-to-molecule generation
• structural constraints
• chemical verification
• error correction
• molecular identity

Innovation

• text-to-molecule generation
• chemical constraint verification
• generator-verifier-refiner framework
• molecular structure refinement
• chemistry-grounded AI