The results suggest that coupling generation with executable verification and feedback-guided refinement is an effective way to improve text-to-molecule generation.
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.
A text-guided molecular graph generation framework that leverages the structural modeling power of graph diffusion models to achieve both strong alignment with textual descriptions and high-quality molecular structures and a molecule structure consistency loss that explicitly enforces structural coherence during genera...
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This work takes PROTAC linker design as a representative case where data scarcity, multi-constraint satisfaction, and interpretability requirements simultaneously hold, and provides quantitative evidence of distributional mismatch between PROTAC linkers and general small-molecule linkers.
Mol-CADiff is introduced, a diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation and enhances dependency modeling both within and across modalities, enabling precise control over the generation process.
TFMat is introduced, a text-conditioned flow-matching framework that uses structured materials language as a semantic prior for a CrystalFlow generator and improves one-candidate match rates over CrystalFlow and reaches a 92.04% MP-20 match rate with 20 candidates.
Recently, Large Language Models (LLMs) have become the dominant paradigm for molecular editing due to their strong generalization capabilities across diverse tasks. However, treating molecules as 1D text strings (SMILES) introduces significant challenges in controllability and structural validity. In this paper, we que...
Jiajun Yu, Zhihao Wu, Yizhen Zheng et al.· Proceedings of the 32nd ACM...· 0 citations
Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity. Existing validation approaches can identify non-computation-ready structures, but they often rely on...
G. Zhao, Xiao-Yan Li· 0 citations
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