@article{RajanBrinkhausSteinbecketal.2024, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Steinbeck, Christoph and Zielesny, Achim}, title = {Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture}, series = {Journal of Cheminformatics}, volume = {2024}, journal = {Journal of Cheminformatics}, number = {16: 78}, doi = {10.1186/s13321-024-00872-7}, year = {2024}, abstract = {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.}, language = {en} } @article{RajanHeinSteinbecketal.2021, author = {Rajan, Kohulan and Hein, Jan-Mathis and Steinbeck, Christoph and Zielesny, Achim}, title = {Molecule Set Comparator (MSC): a CDK-based open rich-client tool for molecule set similarity evaluations}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, issn = {1758-2946}, doi = {10.1186/s13321-021-00485-4}, pages = {Artikelnr. 5}, year = {2021}, language = {en} } @article{RajanWeissenbornLedereretal.2025, author = {Rajan, Kohulan and Weißenborn, Viktor and Lederer, Laurin and Steinbeck, Christoph and Zielesny, Achim}, title = {MARCUS: molecular annotation and recognition for curating unravelled structures}, series = {Digital Discovery}, volume = {2025}, journal = {Digital Discovery}, number = {4}, publisher = {Royal Society of Chemistry}, doi = {10.1039/D5DD00313J}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1010-opus4-49019}, pages = {3137 -- 3148}, year = {2025}, abstract = {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.}, language = {en} } @unpublished{RajanBrinkhausZielesnyetal.2024, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Zielesny, Achim and Steinbeck, Christoph}, title = {Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture}, doi = {10.26434/chemrxiv-2024-7ch9f}, year = {2024}, abstract = {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.}, language = {en} } @unpublished{RajanZielesnySteinbeck2024, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {STOUT V2.0: SMILES to IUPAC name conversion using transformer models}, series = {ChemRxiv}, journal = {ChemRxiv}, doi = {10.26434/chemrxiv-2024-089vs}, year = {2024}, abstract = {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.}, language = {en} } @article{BaenschDanielLanigetal.2024, author = {B{\"a}nsch, Felix and Daniel, Mirco and Lanig, Harald and Steinbeck, Christoph and Zielesny, Achim}, title = {An automated calculation pipeline for differential pair interaction energies with molecular force fields using the Tinker Molecular Modeling Package}, series = {Journal of Cheminformatics}, volume = {16 (2024)}, journal = {Journal of Cheminformatics}, number = {Artikel Nr. 96}, doi = {10.1186/s13321-024-00890-5}, pages = {19 Seiten}, year = {2024}, abstract = {An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.}, language = {en} } @inproceedings{ZielesnyDanielLanigetal.2024, author = {Zielesny, Achim and Daniel, Mirco and Lanig, Harald and Steinbeck, Christoph}, title = {An automated Calculation Pipeline for Differential Pair Interaction Energies with Molecular Force Fields using the Tinker Molecular Modeling Package}, series = {36th Molecular Modeling Workshop, Erlangen, Germany}, booktitle = {36th Molecular Modeling Workshop, Erlangen, Germany}, year = {2024}, abstract = {An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLSAA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.}, language = {en} } @inproceedings{BaenschDanielLanigetal.2022, author = {B{\"a}nsch, Felix and Daniel, Mirco and Lanig, Harald and Steinbeck, Christoph and Zielesny, Achim}, title = {A Calculation Pipeline for Differential Molecule Pair Interaction Energies}, series = {35th Molecular Modeling Workshop}, booktitle = {35th Molecular Modeling Workshop}, year = {2022}, language = {en} } @article{RajanBrinkhausSorokinaetal.2021, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Sorokina, Maria and Zielesny, Achim and Steinbeck, Christoph}, title = {DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, doi = {10.1186/s13321-021-00496-1}, pages = {Artikelnr. 20}, year = {2021}, language = {en} } @article{RajanBrinkhausAgeaetal.2023, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Agea, M. Isabel and Zielesny, Achim and Steinbeck, Christoph}, title = {DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications}, series = {Nature Communications}, volume = {2023}, journal = {Nature Communications}, number = {14: 5045}, doi = {10.1038/s41467-023-40782-0}, year = {2023}, 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.}, language = {en} } @article{BaenschSchaubSevindiketal.2023, author = {B{\"a}nsch, Felix and Schaub, Jonas and Sevindik, Bet{\"u}l and Behr, Samuel and Zander, Julian and Steinbeck, Christoph and Zielesny, Achim}, title = {MORTAR: a rich client application for in silico molecule fragmentation}, series = {Journal of Cheminformatics}, volume = {2023}, journal = {Journal of Cheminformatics}, number = {15}, publisher = {Springer Nature}, issn = {1758-2946}, doi = {https://doi.org/10.1186/s13321-022-00674-9}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1010-opus4-42005}, pages = {14}, year = {2023}, abstract = {Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the rules. This requires a computational workflow with data import, fragmentation algorithm integration, and result visualisation. The described workflow is normally unavailable for a new algorithm and must be set up individually. This work presents an open Java rich client Graphical User Interface (GUI) application to support the development of new in silico molecule fragmentation algorithms and make them readily available upon release. The MORTAR (MOlecule fRagmenTAtion fRamework) application visualises fragmentation results of a set of molecules in various ways and provides basic analysis features. Fragmentation algorithms can be integrated and developed within MORTAR by using a specific wrapper class. In addition, fragmentation pipelines with any combination of the available fragmentation methods can be executed. Upon release, three fragmentation algorithms are already integrated: ErtlFunctionalGroupsFinder, Sugar Removal Utility, and Scaffold Generator. These algorithms, as well as all cheminformatics functionalities in MORTAR, are implemented based on the Chemistry Development Kit (CDK).}, language = {en} } @article{TruszkowskiDanielKuhnetal.2014, author = {Truszkowski, Andreas and Daniel, Mirco and Kuhn, Hubert and Neumann, Stefan and Steinbeck, Christoph and Zielesny, Achim and Epple, Matthias}, title = {A molecular fragment cheminformatics roadmap for mesoscopic simulation}, series = {Journal of Cheminformatics}, volume = {6}, journal = {Journal of Cheminformatics}, number = {Artikelnr. 45}, issn = {1758-2946}, doi = {10.1186/s13321-014-0045-3}, pages = {13}, year = {2014}, language = {en} } @inproceedings{KuhnNeumannSteinbecketal.2009, author = {Kuhn, Hubert and Neumann, Stefan and Steinbeck, Christoph and Wittekindt, Carsten and Zielesny, Achim}, title = {Molecular fragments chemoinformatics}, series = {Journal of Cheminformatics}, volume = {2}, booktitle = {Journal of Cheminformatics}, number = {Suppl 1}, issn = {1758-2946}, doi = {10.1186/1758-2946-2-S1-P14}, pages = {P14}, year = {2009}, language = {en} } @article{KuhnSteinbeckZielesny2010, author = {Kuhn, Thomas and Steinbeck, Christoph and Zielesny, Achim}, title = {Open-Source-Workflows}, series = {Nachrichten aus der Chemie}, volume = {58}, journal = {Nachrichten aus der Chemie}, number = {1}, issn = {1439-9598}, pages = {40 -- 42}, year = {2010}, language = {de} } @unpublished{ZielesnyWeissenbornLedereretal.2025, author = {Zielesny, Achim and Weißenborn, Viktor and Lederer, Laurin and Steinbeck, Christoph and Rajan, Kohulan}, title = {MARCUS: Molecular Annotation and Recognition for Curating Unravelled Structures}, series = {ChemRxiv}, journal = {ChemRxiv}, doi = {10.26434/chemrxiv-2025-9p1q1}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1010-opus4-48310}, year = {2025}, abstract = {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.}, language = {en} } @unpublished{BrinkhausRajanZielesnyetal.2022, author = {Brinkhaus, Henning Otto and Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {RanDepict - Random Chemical Structure Depiction Generator}, series = {ChemRxiv}, journal = {ChemRxiv}, doi = {10.26434/chemrxiv-2022-t1kbb}, year = {2022}, abstract = {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.}, language = {en} } @article{KuhnWillighagenZielesnyetal.2010, author = {Kuhn, Thomas and Willighagen, Egon L. and Zielesny, Achim and Steinbeck, Christoph}, title = {CDK-Taverna: an open workflow environment for cheminformatics}, series = {BMC Bioinformatics}, volume = {11}, journal = {BMC Bioinformatics}, doi = {10.1186/1471-2105-11-159}, pages = {159}, year = {2010}, language = {en} } @article{BrinkhausZielesnySteinbecketal., author = {Brinkhaus, Henning Otto and Zielesny, Achim and Steinbeck, Christoph and Rajan, Kohulan}, title = {DECIMER—hand-drawn molecule images dataset}, series = {Journal of Cheminformatics}, volume = {14.2022}, journal = {Journal of Cheminformatics}, publisher = {BioMed Central}, address = {London}, issn = {1758-2946}, pages = {1 -- 5}, abstract = {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.}, language = {en} } @unpublished{BrinkhausZielesnySteinbecketal.2022, author = {Brinkhaus, Henning Otto and Zielesny, Achim and Steinbeck, Christoph and Rajan, Kohulan}, title = {DECIMER - Hand-drawn molecule images dataset}, year = {2022}, abstract = {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.}, language = {en} } @article{RajanZielesnySteinbeck2021, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {STOUT: SMILES to IUPAC names using neural machine translation}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, issn = {1758-2946}, doi = {10.1186/s13321-021-00512-4}, pages = {Artikelnr. 34}, year = {2021}, language = {en} } @article{KuhnSteinbeckZielesny2010, author = {Kuhn, Thomas and Steinbeck, Christoph and Zielesny, Achim}, title = {Open-Source-Workflows}, series = {Nachrichten aus der Chemie}, volume = {58}, journal = {Nachrichten aus der Chemie}, number = {1}, issn = {1439-9598}, doi = {10.1002/nadc.201070229}, pages = {40 -- 42}, year = {2010}, language = {de} } @inproceedings{RajanBrinkhausZielesnyetal.2022, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Zielesny, Achim and Steinbeck, Christoph}, title = {The DECIMER (Deep lEarning for Chemical IMagE Recognition) project}, series = {17th German Conference on Cheminformatics}, booktitle = {17th German Conference on Cheminformatics}, publisher = {Gesellschaft Deutscher Chemiker}, address = {Frankfurt a.M.}, year = {2022}, language = {en} } @inproceedings{RajanBrinkhausZielesnyetal.2022, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Zielesny, Achim and Steinbeck, Christoph}, title = {The DECIMER (Deep lEarning for Chemical IMagE Recognition) project}, series = {12th International Conference on Chemical Structures}, booktitle = {12th International Conference on Chemical Structures}, year = {2022}, language = {en} } @article{RajanZielesnySteinbeck2020, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {DECIMER: towards deep learning for chemical image recognition}, series = {Journal of Cheminformatics}, journal = {Journal of Cheminformatics}, number = {12}, doi = {10.1186/s13321-020-00469-w}, pages = {65}, year = {2020}, language = {en} } @article{BrinkhausRajanSchaubetal.2023, author = {Brinkhaus, Henning Otto and Rajan, Kohulan and Schaub, Jonas and Zielesny, Achim and Steinbeck, Christoph}, title = {Open data and algorithms for open science in AI-driven molecular informatics}, series = {Current Opinion in Structural Biology}, volume = {2023}, journal = {Current Opinion in Structural Biology}, number = {79}, doi = {https://doi.org/10.1016/j.sbi.2023.102542}, year = {2023}, abstract = {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.}, language = {en} } @article{SchaubZanderZielesnyetal.2022, author = {Schaub, Jonas and Zander, Julian and Zielesny, Achim and Steinbeck, Christoph}, title = {Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)}, series = {Journal of Cheminformatics}, volume = {2022}, journal = {Journal of Cheminformatics}, number = {14:79}, publisher = {BioMed Central}, address = {London}, issn = {1758-2946}, doi = {https://doi.org/10.1186/s13321-022-00656-x}, pages = {25}, year = {2022}, abstract = {The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.}, language = {en} } @article{RajanBrinkhausSorokinaetal.2021, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Sorokina, Maria and Zielesny, Achim and Steinbeck, Christoph}, title = {DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, issn = {1758-2946}, doi = {10.1186/s13321-021-00496-1}, pages = {Artikelnr. 20}, year = {2021}, language = {en} } @article{TruszkowskiNeumannZielesnyetal.2011, author = {Truszkowski, Andreas and Neumann, Stefan and Zielesny, Achim and Willighagen, Egon L. and Steinbeck, Christoph}, title = {CDK-Taverna 2.0: migration and enhancements of an open-source pipelining solution}, series = {Journal of Cheminformatics}, volume = {3}, journal = {Journal of Cheminformatics}, number = {Suppl 1}, issn = {1758-2946}, doi = {10.1186/1758-2946-3-S1-P5}, pages = {P5}, year = {2011}, language = {en} } @inproceedings{BaenschSchaubSevindiketal.2023, author = {B{\"a}nsch, Felix and Schaub, Jonas and Sevindik, Bet{\"u}l and Behr, Samuel and Zander, Julian and Steinbeck, Christoph and Zielesny, Achim}, title = {MORTAR - A Rich Client Application for in silico Molecule Fragmentation}, series = {35th Molecular Modeling Workshop}, booktitle = {35th Molecular Modeling Workshop}, year = {2023}, language = {en} } @article{RajanSteinbeckZielesny, author = {Rajan, Kohulan and Steinbeck, Christoph and Zielesny, Achim}, title = {Performance of chemical structure string representations for chemical image recognition using transformers}, series = {Digital Discovery}, volume = {1.2022}, journal = {Digital Discovery}, number = {1}, publisher = {Royal Society of Chemistry}, address = {Cambridge}, issn = {2635-098X}, pages = {84 -- 90}, abstract = {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.}, language = {en} } @article{BaenschSteinbeckZielesny, author = {B{\"a}nsch, Felix and Steinbeck, Christoph and Zielesny, Achim}, title = {Notes on the Treatment of Charged Particles for Studying Cyclotide/Membrane Interactions with Dissipative Particle Dynamics}, series = {Membranes}, volume = {12.2022}, journal = {Membranes}, number = {6}, publisher = {MDPI}, address = {Basel}, doi = {https://doi.org/10.3390/membranes12060619}, pages = {619}, abstract = {Different charge treatment approaches are examined for cyclotide-induced plasma membrane disruption by lipid extraction studied with dissipative particle dynamics. A pure Coulomb approach with truncated forces tuned to avoid individual strong ion pairing still reveals hidden statistical pairing effects that may lead to artificial membrane stabilization or distortion of cyclotide activity depending on the cyclotide's charge state. While qualitative behavior is not affected in an apparent manner, more sensitive quantitative evaluations can be systematically biased. The findings suggest a charge smearing of point charges by an adequate charge distribution. For large mesoscopic simulation boxes, approximations for the Ewald sum to account for mirror charges due to periodic boundary conditions are of negligible influence.}, language = {en} } @article{FritschNeumannSchaubetal.2019, author = {Fritsch, Sebastian and Neumann, Stefan and Schaub, Jonas and Steinbeck, Christoph and Zielesny, Achim}, title = {ErtlFunctionalGroupsFinder: automated rule-based functional group detection with the Chemistry Development Kit (CDK)}, series = {Journal of Cheminformatics}, volume = {11}, journal = {Journal of Cheminformatics}, doi = {10.1186/s13321-019-0361-8}, pages = {37}, year = {2019}, language = {en} } @inproceedings{BaenschSchaubSteinbecketal.2022, author = {B{\"a}nsch, Felix and Schaub, Jonas and Steinbeck, Christoph and Zielesny, Achim}, title = {MORTAR - An open rich-client framework for in silico molecule fragmentation}, series = {17th German Conference on Cheminformatics}, booktitle = {17th German Conference on Cheminformatics}, publisher = {Gesellschaft Deutscher Chemiker e.V.}, address = {Frankfurt/Main}, year = {2022}, language = {en} } @article{BaenschSteinbeckZielesny2023, author = {B{\"a}nsch, Felix and Steinbeck, Christoph and Zielesny, Achim}, title = {Notes on molecular fragmentation and parameter settings for a dissipative particle dynamics study of a C10E4/water mixture with lamellar bilayer formation}, series = {Journal of Cheminformatics}, volume = {2023}, journal = {Journal of Cheminformatics}, number = {15, 23}, doi = {https://doi.org/10.1186/s13321-023-00697-w}, year = {2023}, abstract = {The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A "bottom-up" decomposition of C10E4 into the smallest fragment molecules (particles) that satisfy chemical intuition leads to convincing simulation results which agree with experimental findings for bilayer formation and thickness. For integration of the equations of motion Shardlow's S1 scheme proves to be a favorable choice with best overall performance. Increasing the integration time steps above the common setting of 0.04 DPD units leads to increasingly unphysical temperature drifts, but also to increasingly rapid formation of bilayer superstructures without significantly distorted particle distributions up to an integration time step of 0.12. A scaling of the mutual particle-particle repulsions that guide the dynamics has negligible influence within a considerable range of values but exhibits apparent lower thresholds beyond which a simulation fails. Repulsion parameter scaling and molecular particle decomposition show a mutual dependence. For mapping of concentrations to molecule numbers in the simulation box particle volume scaling should be taken into account. A repulsion parameter morphing investigation suggests to not overstretch repulsion parameter accuracy considerations.}, language = {en} } @article{RajanZielesnySteinbeck2024, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {STOUT V2.0: SMILES to IUPAC name conversion using transformer models}, series = {Journal of Cheminformatics}, volume = {2024}, journal = {Journal of Cheminformatics}, number = {16:146}, doi = {10.1186/s13321-024-00941-x}, year = {2024}, abstract = {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.}, language = {en} } @article{RajanBrinkhausZielesnyetal.2020, author = {Rajan, Kohulan and Brinkhaus, Henning Otto and Zielesny, Achim and Steinbeck, Christoph}, title = {A review of optical chemical structure recognition tools}, series = {Journal of Cheminformatics}, journal = {Journal of Cheminformatics}, number = {12}, doi = {10.1186/s13321-020-00465-0}, pages = {603}, year = {2020}, language = {en} } @unpublished{RajanZielesnySteinbeck2021, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers}, doi = {10.33774/chemrxiv-2021-9j7wg-v2}, pages = {32}, year = {2021}, language = {en} } @article{SchaubZielesnySteinbecketal.2021, author = {Schaub, Jonas and Zielesny, Achim and Steinbeck, Christoph and Sorokina, Maria}, title = {Description and Analysis of Glycosidic Residues in the Largest Open Natural Products Database}, series = {Biomolecules}, volume = {11}, journal = {Biomolecules}, number = {4}, issn = {2218-273X}, doi = {10.3390/biom11040486}, pages = {486}, year = {2021}, language = {en} } @article{BrinkhausRajanZielesnyetal., author = {Brinkhaus, Henning Otto and Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {RanDepict: Random chemical structure depiction generator}, series = {Journal of Cheminformatics}, volume = {14.2022}, journal = {Journal of Cheminformatics}, number = {31}, publisher = {BioMed Central}, address = {London}, issn = {1758-2946}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @unpublished{SchaubZanderZielesnyetal.2022, author = {Schaub, Jonas and Zander, Julian and Zielesny, Achim and Steinbeck, Christoph}, title = {Scaffold Generator - A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)}, year = {2022}, abstract = {The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.}, language = {en} } @inproceedings{SchaubFritschNeumannetal.2019, author = {Schaub, Jonas and Fritsch, Sebastian and Neumann, Stefan and Steinbeck, Christoph and Zielesny, Achim}, title = {ErtlFunctionalGroupsFinder: automated rule-based functional group detection with the Chemistry Development Kit (CDK)}, series = {Konferenz: 15th German Conference on Cheminformatics GCC 2019, 3.-5. November 2019 in Mainz}, booktitle = {Konferenz: 15th German Conference on Cheminformatics GCC 2019, 3.-5. November 2019 in Mainz}, year = {2019}, language = {en} } @article{KuhnZielesnySteinbeck2008, author = {Kuhn, Thomas and Zielesny, Achim and Steinbeck, Christoph}, title = {Creating chemo- \& bioinformatics workflows, further developments within the CDK-Taverna Project}, series = {Chemistry Central Journal}, volume = {2}, journal = {Chemistry Central Journal}, number = {Suppl 1}, issn = {1752-153X}, doi = {10.1186/1752-153X-2-S1-P27}, pages = {P27}, year = {2008}, language = {en} } @article{SchaubZielesnySteinbecketal.2020, author = {Schaub, Jonas and Zielesny, Achim and Steinbeck, Christoph and Sorokina, Maria}, title = {Too sweet: cheminformatics for deglycosylation in natural products}, series = {Journal of Cheminformatics}, journal = {Journal of Cheminformatics}, number = {12}, doi = {10.1186/s13321-020-00467-y}, pages = {67}, year = {2020}, language = {en} } @inproceedings{RajanZielesnySteinbeck2024, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {Also in Chemistry, Deep Learning Models Love Really Big Data}, series = {Beilstein Bozen Symposium 2024 - AI in Chemistry and Biology: Evolution or Revolution?, R{\"u}desheim, Germany}, booktitle = {Beilstein Bozen Symposium 2024 - AI in Chemistry and Biology: Evolution or Revolution?, R{\"u}desheim, Germany}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{BaenschSteinbeckZielesny2019, author = {B{\"a}nsch, Felix and Steinbeck, Christoph and Zielesny, Achim}, title = {Towards a comprehensive open computational support cycle for Molecular Fragment Dissipative Particle Dynamics (DPD)}, series = {Konferenz: 15th German Conference on Cheminformatics GCC 2019, 3.-5. November 2019 in Mainz}, booktitle = {Konferenz: 15th German Conference on Cheminformatics GCC 2019, 3.-5. November 2019 in Mainz}, year = {2019}, language = {en} } @inproceedings{SchaubBaenschZielesnyetal.2020, author = {Schaub, Jonas and B{\"a}nsch, Felix and Zielesny, Achim and Steinbeck, Christoph}, title = {Rule-based in-silico Fragmentation for the Analysis of Natural Product Chemical Space}, series = {Konferenz: 34th Molecular Modelling Workshop 2020, 17.-19. Februar 2020 in Erlangen}, booktitle = {Konferenz: 34th Molecular Modelling Workshop 2020, 17.-19. Februar 2020 in Erlangen}, year = {2020}, language = {en} } @article{RajanZielesnySteinbeck2021, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {DECIMER 1.0: deep learning for chemical image recognition using transformers}, series = {Journal of Cheminformatics}, volume = {13}, journal = {Journal of Cheminformatics}, issn = {1758-2946}, doi = {10.1186/s13321-021-00538-8}, pages = {Artikelnr. 61}, year = {2021}, language = {en} } @unpublished{RajanSteinbeckZielesny2021, author = {Rajan, Kohulan and Steinbeck, Christoph and Zielesny, Achim}, title = {Performance of chemical structure string representations for chemical image recognition using transformers}, doi = {10.33774/chemrxiv-2021-7c9wf-v2}, pages = {19}, year = {2021}, language = {en} } @inproceedings{RajanZielesnySteinbeck2024, author = {Rajan, Kohulan and Zielesny, Achim and Steinbeck, Christoph}, title = {The DECIMER.ai Project}, series = {Workshop Computer Vision for Science (CV4Science) at the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), June 17, 2024, Seattle, USA}, booktitle = {Workshop Computer Vision for Science (CV4Science) at the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), June 17, 2024, Seattle, USA}, year = {2024}, abstract = {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.}, language = {en} } @unpublished{BaenschDanielLanigetal.2024, author = {B{\"a}nsch, Felix and Daniel, Mirco and Lanig, Harald and Steinbeck, Christoph and Zielesny, Achim}, title = {An automated Calculation Pipeline for Differential Pair Interaction Energies with Molecular Force Fields using the Tinker Molecular Modeling Package}, doi = {10.26434/chemrxiv-2024-pkmxm}, year = {2024}, abstract = {An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.}, language = {en} } @inproceedings{TruszkowskiNeumannZielesnyetal.2011, author = {Truszkowski, Andreas and Neumann, Stefan and Zielesny, Achim and Willighagen, Egon L. and Steinbeck, Christoph}, title = {Reaction enumeration and machine learning enhancements for the open-source pipelining solution CDK-Taverna 2.0}, series = {Konferenz: 9th International Conference on Chemical Structures (ICCS). 5.-9. Juni 2011 in Noordwijkerhout, Niederlande}, booktitle = {Konferenz: 9th International Conference on Chemical Structures (ICCS). 5.-9. Juni 2011 in Noordwijkerhout, Niederlande}, year = {2011}, language = {en} } @inproceedings{ZielesnyRajanSteinbeck2025, author = {Zielesny, Achim and Rajan, Kohulan and Steinbeck, Christoph}, title = {Comparative Analysis of Chemical Structure String Representations for Neural Machine Translation}, series = {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}, volume = {2025}, booktitle = {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}, publisher = {Springer}, doi = {10.1007/978-3-032-04552-2_2}, pages = {8 -- 16}, year = {2025}, abstract = {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.}, language = {en} } @article{TruszkowskiJayaseelanNeumannetal.2011, author = {Truszkowski, Andreas and Jayaseelan, Kalai Vanii and Neumann, Stefan and Willighagen, Egon L. and Zielesny, Achim and Steinbeck, Christoph}, title = {New developments on the cheminformatics open workflow environment CDK-Taverna}, series = {Journal of Cheminformatics}, volume = {3}, journal = {Journal of Cheminformatics}, number = {Artikelnr. 54}, issn = {1758-2946}, doi = {10.1186/1758-2946-3-54}, pages = {10}, year = {2011}, language = {en} } @inproceedings{BaenschDanielLanigetal.2020, author = {B{\"a}nsch, Felix and Daniel, Mirco and Lanig, Harald and Steinbeck, Christoph and Zielesny, Achim}, title = {A new Approach to DPD Repulsion Parameter Estimation}, series = {Konferenz: 34th Molecular Modelling Workshop 2020, 17.-19. Februar 2020 in Erlangen}, booktitle = {Konferenz: 34th Molecular Modelling Workshop 2020, 17.-19. Februar 2020 in Erlangen}, year = {2020}, language = {en} } @inproceedings{KuhnWillighagenZielesnyetal.2006, author = {Kuhn, Thomas and Willighagen, Egon L. and Zielesny, Achim and Steinbeck, Christoph}, title = {Creating Chemo- and Bioinformatics Workflows - The CDK-Taverna Project}, series = {Konferenz: 2nd German Conference of Chemoinformatics. 12.-14. November 2006 in Goslar}, booktitle = {Konferenz: 2nd German Conference of Chemoinformatics. 12.-14. November 2006 in Goslar}, year = {2006}, language = {en} }