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Published on: December 1, 2023
Dual-Path Global Awareness Transformer for Optical Chemical Structure Recognition
Rui Wang1, Yujin Ji2, Youyong Li1,2
1Macao Institute of Materials Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China.
This study introduces the dual-path global awareness transformer (DGAT) to improve optical chemical structure recognition (OCSR). DGAT enhances sequence generation accuracy for complex chemical structures, achieving state-of-the-art results.
Area of Science:
- Materials Science
- Computer Science
- Cheminformatics
Background:
- Optical chemical structure recognition (OCSR) reconstructs chemical graphics into machine-readable sequences.
- Current multimodal fusion methods struggle with global context and complex motifs, leading to sequence errors.
- Challenges include recognizing intricate structures like rings and long chains accurately.
Purpose of the Study:
- To develop an advanced model for accurate OCSR sequence generation.
- To overcome limitations of existing methods in capturing global context and handling complex chemical motifs.
- To improve the symbolic accuracy and chemical precision of generated sequences.
Main Methods:
- Proposed the dual-path global awareness transformer (DGAT) model.
- Introduced a cascaded global feature enhancement (CGFE) module for emphasizing global context and bridging cross-modal gaps.
- Implemented a sparse differential global-local attention (SDGLA) module for dynamic capture of global-local feature differences.
Main Results:
- DGAT achieved state-of-the-art (SOTA) performance on a newly constructed evaluation dataset.
- Achieved BLEU-4 score of 0.840 (+5.3%), ROUGE-L score of 0.908 (+1.9%), and mean Tanimoto similarity of 0.988 (+1.2%) over the best published model.
- Demonstrated superior ability in generating symbolically accurate and chemically precise sequences.
Conclusions:
- The proposed DGAT model significantly advances OCSR capabilities.
- DGAT effectively addresses the limitations of previous methods in handling complex chemical structures.
- The model's performance confirms its potential for precise chemical information extraction from graphics.
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