TY - JOUR A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture JF - Journal of Cheminformatics N2 - 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. KW - DECIMER KW - Hand-drawn chemical structures KW - OCSR, Optical Chemical Structure Recognition KW - Transformer KW - Deep Learning Y1 - 2024 U6 - https://doi.org/10.1186/s13321-024-00872-7 VL - 2024 IS - 16: 78 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Hein, Jan-Mathis A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - Molecule Set Comparator (MSC): a CDK-based open rich‐client tool for molecule set similarity evaluations JF - Journal of Cheminformatics Y1 - 2021 U6 - https://doi.org/10.1186/s13321-021-00485-4 SN - 1758-2946 VL - 13 SP - Artikelnr. 5 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Weißenborn, Viktor A1 - Lederer, Laurin A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - MARCUS: molecular annotation and recognition for curating unravelled structures JF - Digital Discovery N2 - The exponential growth of chemical literature necessitates the development of automated tools for extracting and curating molecular information from unstructured scientific publications into open-access chemical databases. Current optical chemical structure recognition (OCSR) and named entity recognition solutions operate in isolation, which limits their scalability for comprehensive literature curation. Here we present MARCUS (Molecular Annotation and Recognition for Curating Unravelled Structures), a tool designed for natural product literature curation that integrates COCONUT-aware schema mapping, CIP-based stereochemical validation, and human-in-the-loop structure refinement. This integrated web-based platform combines automated text annotation, multi-engine OCSR, and direct submission capabilities to the COCONUT database. MARCUS employs a fine-tuned GPT-4 model to extract chemical entities and utilises a Human-in-the-loop ensemble approach integrating DECIMER, MolNexTR, and MolScribe for structure recognition. The platform aims to streamline the data extraction workflow from PDF upload to database submission, significantly reducing curation time. MARCUS bridges the gap between unstructured chemical literature and machine-actionable databases, enabling FAIR data principles and facilitating AI-driven chemical discovery. Through open-source code, accessible models, and comprehensive documentation, the web application enhances accessibility and promotes community-driven development. This approach facilitates unrestricted use and encourages the collaborative advancement of automated chemical literature curation tools. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1010-opus4-49019 VL - 2025 IS - 4 SP - 3137 EP - 3148 PB - Royal Society of Chemistry ER - TY - INPR A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture N2 - 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. KW - OCSR, Optical Chemical Structure Recognition KW - DECIMER KW - Deep Learning KW - Transformer Y1 - 2024 U6 - https://doi.org/10.26434/chemrxiv-2024-7ch9f ER - TY - INPR A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - STOUT V2.0: SMILES to IUPAC name conversion using transformer models T2 - ChemRxiv N2 - Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge. Accurate translation of chemical compounds from SMILES notation into their corresponding IUPAC names is crucial, as it can significantly streamline the laborious process of naming chemical structures. Here, we present STOUT (SMILES-TO-IUPAC-name translator) V2.0, which addresses this challenge by introducing a transformer-based model that translates string representations of chemical structures into IUPAC names. Trained on a dataset of nearly 1 billion SMILES strings and their corresponding IUPAC names, STOUT V2.0 demonstrates exceptional accuracy in generating IUPAC names, even for complex chemical structures. The model's ability to capture intricate patterns and relationships within chemical structures enables it to generate precise and standardised IUPAC names. Deterministic algorithms for systematically naming chemical structures have been available for many years. Also, this work has only been possible through an academic license for OpenEye’s Lexichem software. KW - Transformers KW - Deep Learning KW - Artificial Intelligence KW - Chemical name translation Y1 - 2024 U6 - https://doi.org/10.26434/chemrxiv-2024-089vs ER - TY - JOUR A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Sorokina, Maria A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature JF - Journal of Cheminformatics Y1 - 2021 U6 - https://doi.org/10.1186/s13321-021-00496-1 VL - 13 SP - Artikelnr. 20 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Agea, M. Isabel A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications JF - Nature Communications N2 - 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. KW - machine learning KW - artificial intelligence KW - AI KW - optical chemical structure recognition KW - OCSR Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-40782-0 VL - 2023 IS - 14: 5045 ER - TY - INPR A1 - Zielesny, Achim A1 - Weißenborn, Viktor A1 - Lederer, Laurin A1 - Steinbeck, Christoph A1 - Rajan, Kohulan T1 - MARCUS: Molecular Annotation and Recognition for Curating Unravelled Structures T2 - ChemRxiv N2 - The exponential growth of chemical literature necessitates the development of automated tools for extracting and curating molecular information from unstructured scientific publications into open-access chemical databases. Current optical chemical structure recognition (OCSR) and named entity recognition solutions operate in isolation, which limits their scalability for comprehensive literature curation. Here we present MARCUS (Molecular Annotation and Recognition for Curating Unravelled Structures), a tool to aid curators in performing literature curation in the field of natural products. This integrated web-based platform combines automated text annotation, multi-engine OCSR, and direct submission capabilities to the COCONUT database. MARCUS employs a fine-tuned GPT-4 model to extract chemical entities and utilises an ensemble approach integrating DECIMER, MolNexTR, and MolScribe for structure recognition. The platform aims to streamline the data extraction workflow from PDF upload to database submission, significantly reducing curation time. MARCUS bridges the gap between unstructured chemical literature and machine-actionable databases, enabling FAIR data principles and facilitating AI-driven chemical discovery. Through open-source code, accessible models, and comprehensive documentation, the web application enhances accessibility and promotes community-driven development. This approach facilitates unrestricted use and encourages the collaborative advancement of automated chemical literature curation tools. We dedicate MARCUS to Dr Marcus Ennis, the longest-serving curator of the ChEBI database, on the occasion of his 75th birthday. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1010-opus4-48310 ER - TY - INPR A1 - Brinkhaus, Henning Otto A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - RanDepict - Random Chemical Structure Depiction Generator T2 - ChemRxiv N2 - 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. Y1 - 2022 U6 - https://doi.org/10.26434/chemrxiv-2022-t1kbb ER - TY - JOUR A1 - Brinkhaus, Henning Otto A1 - Zielesny, Achim A1 - Steinbeck, Christoph A1 - Rajan, Kohulan T1 - DECIMER—hand-drawn molecule images dataset JF - Journal of Cheminformatics N2 - 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. Y1 - 2022 SN - 1758-2946 VL - 14.2022 SP - 1 EP - 5 PB - BioMed Central CY - London ER - TY - INPR A1 - Brinkhaus, Henning Otto A1 - Zielesny, Achim A1 - Steinbeck, Christoph A1 - Rajan, Kohulan T1 - DECIMER - Hand-drawn molecule images dataset N2 - 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. KW - OCSR KW - Hand-drawn images KW - Molecule images KW - Deep learning KW - Chemical structure depictions KW - Optical Chemical Structure Recognition Y1 - 2022 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - STOUT: SMILES to IUPAC names using neural machine translation JF - Journal of Cheminformatics Y1 - 2021 U6 - https://doi.org/10.1186/s13321-021-00512-4 SN - 1758-2946 VL - 13 SP - Artikelnr. 34 ER - TY - CHAP A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - The DECIMER (Deep lEarning for Chemical IMagE Recognition) project BT - May 8 -12, 2022 in Garmisch-Partenkirchen/Germany T2 - 17th German Conference on Cheminformatics Y1 - 2022 PB - Gesellschaft Deutscher Chemiker CY - Frankfurt a.M. ER - TY - CHAP A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - The DECIMER (Deep lEarning for Chemical IMagE Recognition) project BT - 12-16 June 2022, Noordwijkerhout, The Netherlands T2 - 12th International Conference on Chemical Structures Y1 - 2022 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - DECIMER: towards deep learning for chemical image recognition JF - Journal of Cheminformatics Y1 - 2020 U6 - https://doi.org/10.1186/s13321-020-00469-w IS - 12 SP - 65 ER - TY - JOUR A1 - Brinkhaus, Henning Otto A1 - Rajan, Kohulan A1 - Schaub, Jonas A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - Open data and algorithms for open science in AI-driven molecular informatics JF - Current Opinion in Structural Biology N2 - 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. Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1016/j.sbi.2023.102542 VL - 2023 IS - 79 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Sorokina, Maria A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature JF - Journal of Cheminformatics Y1 - 2021 U6 - https://doi.org/10.1186/s13321-021-00496-1 SN - 1758-2946 VL - 13 SP - Artikelnr. 20 ER - TY - THES A1 - Rajan, Kohulan T1 - DARLING: Deep leARning for chemicaL Information processING KW - Deep learning KW - Datensatz KW - Naturstoff Y1 - 2021 N1 - DOI: https://doi.org/10.22032/dbt.50275 URN: urn:nbn:de:gbv:27-dbt-20211115-144725-009 PB - Friedrich-Schiller-Universität CY - Jena ER - TY - JOUR A1 - Rajan, Kohulan A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - Performance of chemical structure string representations for chemical image recognition using transformers JF - Digital Discovery N2 - The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public. Y1 - 2022 SN - 2635-098X VL - 1.2022 IS - 1 SP - 84 EP - 90 PB - Royal Society of Chemistry CY - Cambridge ER - TY - JOUR A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - STOUT V2.0: SMILES to IUPAC name conversion using transformer models JF - Journal of Cheminformatics N2 - Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge. Accurate translation of chemical compounds from SMILES notation into their corresponding IUPAC names is crucial, as it can significantly streamline the laborious process of naming chemical structures. Here, we present STOUT (SMILES-TO-IUPAC-name translator) V2, which addresses this challenge by introducing a transformer-based model that translates string representations of chemical structures into IUPAC names. Trained on a dataset of nearly 1 billion SMILES strings and their corresponding IUPAC names, STOUT V2 demonstrates exceptional accuracy in generating IUPAC names, even for complex chemical structures. The model’s ability to capture intricate patterns and relationships within chemical structures enables it to generate precise and standardised IUPAC names. While established deterministic algorithms remain the gold standard for systematic chemical naming, our work, enabled by access to OpenEye’s Lexichem software through an academic license, demonstrates the potential of neural approaches to complement existing tools in chemical nomenclature. KW - Artificial Intelligence KW - Machine Learning KW - Transformer KW - SMILES KW - IUPAC names Y1 - 2024 U6 - https://doi.org/10.1186/s13321-024-00941-x VL - 2024 IS - 16:146 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Brinkhaus, Henning Otto A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - A review of optical chemical structure recognition tools JF - Journal of Cheminformatics Y1 - 2020 U6 - https://doi.org/10.1186/s13321-020-00465-0 IS - 12 SP - 603 ER - TY - INPR A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers Y1 - 2021 U6 - https://doi.org/10.33774/chemrxiv-2021-9j7wg-v2 ER - TY - JOUR A1 - Brinkhaus, Henning Otto A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - RanDepict: Random chemical structure depiction generator JF - Journal of Cheminformatics N2 - 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. KW - CDK KW - Chemical image depiction KW - Depiction generator image augmentation KW - Indigo KW - RDKit KW - OCSR Y1 - 2022 SN - 1758-2946 VL - 14.2022 IS - 31 SP - 1 EP - 7 PB - BioMed Central CY - London ER - TY - CHAP A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - Also in Chemistry, Deep Learning Models Love Really Big Data T2 - Beilstein Bozen Symposium 2024 - AI in Chemistry and Biology: Evolution or Revolution?, Rüdesheim, Germany N2 - Inspired by the super-human performance of deep learning models in playing the game of Go after being presented with virtually unlimited training data, we looked into areas in chemistry where similar situations could be achieved. Encountering large amounts of training data in chemistry is still rare, so we turned to two areas where realistic training data can be fabricated in large quantities, namely a) the recognition of machine-readable structures from images of chemical diagrams and b) the conversion of IUPAC(-like) names into structures and vice versa. In this talk, we outline the challenges, technical implementation and results of this study. Optical Chemical Structure Recognition (OCSR): Vast amounts of chemical information remain hidden in the primary literature and have yet to be curated into open-access databases. To automate the process of extracting chemical structures from scientific papers, we developed the DECIMER.ai project. This open-source platform provides an integrated solution for identifying, segmenting, and recognising chemical structure depictions in scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilises a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string. DECIMER.ai is data-driven, relying solely on the training data to make accurate predictions without hand-coded rules or assumptions. The latest model was trained with 127 million structures and 483 million depictions (4 different per structure) on Google TPU-V4 VMs Name to Structure Conversion: The conversion of structures to IUPAC(-like) or systematic names has been solved algorithmically or rule-based in satisfying ways. This fact, on the other side, provided us with an opportunity to generate a name-structure training pair at a very large scale to train a proof-of-concept transformer network and evaluate its performance. In this work, the largest model was trained using almost one billion SMILES strings. The Lexichem software utility from OpenEye was employed to generate the IUPAC names used in the training process. STOUT V2 was trained on Google TPU-V4 VMs. The model's accuracy was validated through one-to-one string matching, BLEU scores, and Tanimoto similarity calculations. To further verify the model's reliability, every IUPAC name generated by STOUT V2 was analysed for accuracy and retranslated using OPSIN, a widely used open-source software for converting IUPAC names to SMILES. This additional validation step confirmed the high fidelity of STOUT V2's translations. Y1 - 2024 ER - TY - JOUR A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - DECIMER 1.0: deep learning for chemical image recognition using transformers JF - Journal of Cheminformatics Y1 - 2021 U6 - https://doi.org/10.1186/s13321-021-00538-8 SN - 1758-2946 VL - 13 SP - Artikelnr. 61 ER - TY - INPR A1 - Rajan, Kohulan A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - Performance of chemical structure string representations for chemical image recognition using transformers Y1 - 2021 U6 - https://doi.org/10.33774/chemrxiv-2021-7c9wf-v2 ER - TY - CHAP A1 - Rajan, Kohulan A1 - Zielesny, Achim A1 - Steinbeck, Christoph T1 - The DECIMER.ai Project T2 - Workshop Computer Vision for Science (CV4Science) at the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), June 17, 2024, Seattle, USA N2 - Over the past few decades, the number of publications describing chemical structures and their metadata has increased significantly. Chemists have published the majority of this information as bitmap images along with other important information as human-readable text in printed literature and have never been retained and preserved in publicly available databases as machine-readable formats. Manually extracting such data from printed literature is error-prone, time-consuming, and tedious. The recognition and translation of images of chemical structures from printed literature into machine-readable format is known as Optical Chemical Structure Recognition (OCSR). In recent years, deep-learning-based OCSR tools have become increasingly popular. While many of these tools claim to be highly accurate, they are either unavailable to the public or proprietary. Meanwhile, the available open-source tools are significantly time-consuming to set up. Furthermore, none of these offers an end-to-end workflow capable of detecting chemical structures, segmenting them, classifying them, and translating them into machine-readable formats. To address this issue, we present the DECIMER.ai project, an open-source platform that provides an integrated solution for identifying, segmenting, and recognizing chemical structure depictions within the scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilizes a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string. A key strength of DECIMER.ai is that its algorithms are data-driven, relying solely on the training data to make accurate predictions without any hand-coded rules or assumptions. By offering this comprehensive, open-source, and transparent pipeline, DECIMER.ai enables automated extraction and representation of chemical data from unstructured publications, facilitating applications in chemoinformatics and drug discovery. Y1 - 2024 ER - TY - CHAP A1 - Zielesny, Achim A1 - Rajan, Kohulan A1 - Steinbeck, Christoph T1 - Comparative Analysis of Chemical Structure String Representations for Neural Machine Translation T2 - Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special sessions : 34th International Conference on Artificial Neural Networks, Kaunas, Lithuania, September 9-12, 2025, Proceedings, Part V. Lecture Notes in Computer Science (LNCS), vol 16072 N2 - In this work, we present a comparative analysis of SMILES, DeepSMILES, and SELFIES string representations for chemical struc-tures in neural machine translation tasks in cheminformatics. Using transformer-based models, we systematically evaluated their effective-ness in translating between these representations and the correspond-ing linguistic IUPAC nomenclature. The experimental results demon-strate comparable performance for all three string representations, with SMILES achieving a marginally higher accuracy (99.30% with stereo-chemical information, 99.21% without) compared to its alternatives. In scaling experiments with 1, 10, and 50 million compounds, the perfor-mance differences remained small, though the performance gap narrowed with larger datasets. These findings suggest that researchers can con-fidently continue using SMILES for neural machine translation tasks with transformers, which benefits from their extensive support in exist-ing chemical libraries, tools, and databases, rather than adopting newer representations. This work has a significant impact on developing more efficient chemical language models in drug discovery, material science, and chemical database curation. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-04552-2_2 VL - 2025 SP - 8 EP - 16 PB - Springer ER -