ViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision

2026-09-27

AI Summary

Error propagation in chemical structure recognition severely compromises data quality, while manual correction remains prohibitively costly. This work proposes a “reject-before-repair” framework that enhances output reliability through spatial alignment verification and executable revision. Methodologically, the approach establishes explicit observation-prediction correspondences and leverages index anchoring to perform localized graph edits rather than full regeneration. It further integrates coordinate-preserving rendering, progressive training of vision-language models, and a selective structure recognition strategy. Experimental results demonstrate that the proposed method achieves an acceptance accuracy exceeding 97%, substantially improving downstream reaction extraction and prediction performance.

Abstract

Reliable optical chemical structure recognition (OCSR) is essential for building high-quality chemical data from scientific literature, yet even small recognition errors can propagate into chemical databases and downstream models. In practice, recognized structures often require manual inspection and correction before use, making large-scale data curation costly and difficult to scale. We therefore study Selective Structure Recognition (SSR), a post-recognition setting that automatically produces reliable structured outputs while rejecting unresolved cases. Selection-only approaches can improve reliability by rejection, but cannot create additional correct outputs beyond those produced by the base recognizer. We propose ViCoR, a repair-before-rejection framework for iterative VerIfiCatiOn and Revision. Its key idea is to make observation-prediction correspondence explicit: coordinate-preserving rendering establishes spatial correspondence between the source image and predicted structure, while index anchoring maps localized visual discrepancies to executable graph edits without full-structure regeneration. A shared VLM is progressively trained from verification to revision. On two real-world OCSR benchmarks, ViCoR improves overall accuracy from 73.53\% to 88.26\% and from 61.83\% to 84.32\%, while achieving over 97\% accepted accuracy at 85--89\% coverage. The resulting molecular data further improve reaction-extraction F1 by 15.5 points and literature-sourced reaction prediction accuracy by 7.7 and 5.8 points, demonstrating the value of automated reliability control for scientific data curation and downstream chemical learning.

Problem

• Optical Chemical Structure Recognition
• Selective Structure Recognition
• Molecular Structure Extraction
• Data Curation
• Reliability

Innovation

• Optical Chemical Structure Recognition
• Selective Structure Recognition
• Vision-Language Model
• Executable Graph Revision
• Spatial Alignment