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• W4206029367 abstract "Abstract The rise of deep learning in various scientific and technology areas promotes the development of AI‐based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer‐based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R‐groups), and visual contamination. We demonstrate that the Transformer‐based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at Syntelly platform, and the code for dataset generation is available on GitHub." @default.
• W4206029367 created "2022-01-25" @default.
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• W4206029367 date "2022-01-01" @default.
• W4206029367 modified "2023-10-10" @default.
• W4206029367 title "Image2SMILES: Transformer‐Based Molecular Optical Recognition Engine**" @default.
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• W4206029367 doi "https://doi.org/10.1002/cmtd.202100069" @default.
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