COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training

📰 ArXiv cs.AI

Learn how COMO, a closed-loop optical molecule recognition system, achieves minimum risk training for translating molecular images into machine-readable representations

advanced Published 28 Apr 2026

Action Steps

Implement COMO using a deep learning framework to recognize molecular structures in images
Train the model using minimum risk training to reduce the impact of visual noise and variations in chemical structures
Evaluate the performance of COMO on a dataset of molecular images and compare it to existing approaches
Apply COMO to real-world documents to translate molecular images into SMILES strings or molecular graphs
Configure the model to handle inexhaustible variations in chemical structures and shorthand conventions

Who Needs to Know This

Researchers and developers in the field of computer vision and chemistry can benefit from this article, as it presents a novel approach to optical chemical structure recognition

Key Insight

💡 COMO achieves state-of-the-art results in optical chemical structure recognition by using a closed-loop approach with minimum risk training

Key Takeaways

Learn how COMO, a closed-loop optical molecule recognition system, achieves minimum risk training for translating molecular images into machine-readable representations

Full Article

Title: COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training

Abstract:
arXiv:2604.23546v1 Announce Type: cross Abstract: Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations like SMILES strings or molecular graphs, but remains challenging in real-world documents due to inexhaustible variations in chemical structures, shorthand conventions, and visual noise. Most existing deep-learning-based approaches rely on teacher forcing with token-level Maximum Likelihood Estimation (MLE). This training paradigm suffer

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