Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model

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This is a Quantitative Study study.

Yufan Chen; Ching Ting Leung; Jianwei Sun; Yong Huang; Linyan Li; Hao Chen; Hanyu Gao

doi:10.1039/d5sc04173b

January 1, 2025Chemical ScienceOpen Access

Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model

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Key Points

AI shows promise in enhancing chemical research through effective image parsing of reactions.
High-quality data availability is essential for training AI models, impacting their effectiveness in chemistry.
Current research highlights a multimodal approach that integrates multiple data sources for better analysis.
The findings underscore the significance of reliable chemical reaction datasets in the advancement of AI applications.

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Implication

Analysis reveals AI's potential in parsing chemical reaction images for organic chemistry, suggesting data quality is crucial.

Abstract

Artificial intelligence (AI) has demonstrated significant promise in advancing organic chemistry research; however, its effectiveness depends on the availability of high-quality chemical reaction data. Currently, most published chemical reactions are...

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Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68e6860af44b9035634c2090 https://doi.org/10.1039/d5sc04173b

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Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model

Key Points

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Implication

Abstract

Discussion

Cite This Study

Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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