DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extra...
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| Vydáno v: | Nature communications Ročník 14; číslo 1; s. 5045 - 18 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London
Nature Publishing Group UK
19.08.2023
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2041-1723, 2041-1723 |
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| Abstract | The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing,
DECIMER.ai
(Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the
DECIMER
web application is available at
https://decimer.ai
.
Chemical structures are typically published as nonmachine-readable images in scientific literature. Here, the authors present DECIMER.ai, an open platform for translating chemical structures in publications into machine-readable representations. |
|---|---|
| AbstractList | Abstract The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai . The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai. Chemical structures are typically published as nonmachine-readable images in scientific literature. Here, the authors present DECIMER.ai, an open platform for translating chemical structures in publications into machine-readable representations. The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai . The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai . Chemical structures are typically published as nonmachine-readable images in scientific literature. Here, the authors present DECIMER.ai, an open platform for translating chemical structures in publications into machine-readable representations. The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai .The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai . |
| ArticleNumber | 5045 |
| Author | Zielesny, Achim Agea, M. Isabel Rajan, Kohulan Brinkhaus, Henning Otto Steinbeck, Christoph |
| Author_xml | – sequence: 1 givenname: Kohulan orcidid: 0000-0003-1066-7792 surname: Rajan fullname: Rajan, Kohulan organization: Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena – sequence: 2 givenname: Henning Otto orcidid: 0000-0002-6664-2183 surname: Brinkhaus fullname: Brinkhaus, Henning Otto organization: Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena – sequence: 3 givenname: M. Isabel orcidid: 0000-0002-3017-7742 surname: Agea fullname: Agea, M. Isabel organization: Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague – sequence: 4 givenname: Achim orcidid: 0000-0003-0722-4229 surname: Zielesny fullname: Zielesny, Achim organization: Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences – sequence: 5 givenname: Christoph orcidid: 0000-0001-6966-0814 surname: Steinbeck fullname: Steinbeck, Christoph email: christoph.steinbeck@uni-jena.de organization: Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena |
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| DOI | 10.1038/s41467-023-40782-0 |
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| Snippet | The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information... Abstract The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical... |
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| Title | DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications |
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