<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>4541</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>16: 78</issue>
    <volume>2024</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-07-15</completedDate>
    <publishedDate>2024-07-05</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture</title>
    <abstract language="eng">Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.</abstract>
    <parentTitle language="eng">Journal of Cheminformatics</parentTitle>
    <identifier type="doi">10.1186/s13321-024-00872-7</identifier>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Kohulan Rajan</author>
    <author>Henning Otto Brinkhaus</author>
    <author>Christoph Steinbeck</author>
    <author>Achim Zielesny</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DECIMER</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hand-drawn chemical structures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OCSR, Optical Chemical Structure Recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transformer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4542</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-07-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture</title>
    <abstract language="eng">Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. To evaluate the model's performance, a benchmark was performed using a real-world dataset of hand-drawn chemical structures. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.</abstract>
    <identifier type="doi">10.26434/chemrxiv-2024-7ch9f</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Kohulan Rajan</author>
    <author>Henning Otto Brinkhaus</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OCSR, Optical Chemical Structure Recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DECIMER</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transformer</value>
    </subject>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>3975</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>Artikelnr. 20</pageFirst>
    <pageLast/>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume>13</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-11-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature</title>
    <parentTitle language="eng">Journal of Cheminformatics</parentTitle>
    <identifier type="doi">10.1186/s13321-021-00496-1</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Kohulan Rajan</author>
    <author>Henning Otto Brinkhaus</author>
    <author>Maria Sorokina</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4477</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>14: 5045</issue>
    <volume>2023</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-09-27</completedDate>
    <publishedDate>2023-09-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Nature Communications</parentTitle>
    <identifier type="doi">10.1038/s41467-023-40782-0</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Kohulan Rajan</author>
    <author>Henning Otto Brinkhaus</author>
    <author>M. Isabel Agea</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optical chemical structure recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OCSR</value>
    </subject>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4112</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">RanDepict - Random Chemical Structure Depiction Generator</title>
    <abstract language="eng">The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.</abstract>
    <parentTitle language="eng">ChemRxiv</parentTitle>
    <identifier type="doi">10.26434/chemrxiv-2022-t1kbb</identifier>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Henning Otto Brinkhaus</author>
    <author>Kohulan Rajan</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4114</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>5</pageLast>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume>14.2022</volume>
    <type>article</type>
    <publisherName>BioMed Central</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-06-09</completedDate>
    <publishedDate>2022-06-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DECIMER—hand-drawn molecule images dataset</title>
    <abstract language="eng">The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER — Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.</abstract>
    <parentTitle language="eng">Journal of Cheminformatics</parentTitle>
    <identifier type="issn">1758-2946</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Henning Otto Brinkhaus</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <author>Kohulan Rajan</author>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4110</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-04-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DECIMER - Hand-drawn molecule images dataset</title>
    <abstract language="eng">The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.</abstract>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Henning Otto Brinkhaus</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <author>Kohulan Rajan</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OCSR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hand-drawn images</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Molecule images</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Chemical structure depictions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optical Chemical Structure Recognition</value>
    </subject>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4118</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Gesellschaft Deutscher Chemiker</publisherName>
    <publisherPlace>Frankfurt a.M.</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-06-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The DECIMER (Deep lEarning for Chemical IMagE Recognition) project</title>
    <parentTitle language="eng">17th German Conference on Cheminformatics</parentTitle>
    <subTitle language="eng">May 8 -12, 2022 in Garmisch-Partenkirchen/Germany</subTitle>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Kohulan Rajan</author>
    <author>Henning Otto Brinkhaus</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4117</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-06-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The DECIMER (Deep lEarning for Chemical IMagE Recognition) project</title>
    <parentTitle language="eng">12th International Conference on Chemical Structures</parentTitle>
    <subTitle language="eng">12-16 June 2022, Noordwijkerhout, The Netherlands</subTitle>
    <licence>© Alle Rechte vorbehalten</licence>
    <author>Kohulan Rajan</author>
    <author>Henning Otto Brinkhaus</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
  <doc>
    <id>4210</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>79</issue>
    <volume>2023</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-02-17</completedDate>
    <publishedDate>2023-02-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Open data and algorithms for open science in AI-driven molecular informatics</title>
    <abstract language="eng">Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.</abstract>
    <parentTitle language="eng">Current Opinion in Structural Biology</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.sbi.2023.102542</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - Namensnennung</licence>
    <author>Henning Otto Brinkhaus</author>
    <author>Kohulan Rajan</author>
    <author>Jonas Schaub</author>
    <author>Achim Zielesny</author>
    <author>Christoph Steinbeck</author>
    <collection role="institutes" number="">Institut für biologische und chemische Informatik</collection>
    <thesisPublisher>Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen</thesisPublisher>
  </doc>
</export-example>
