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 - 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 - Bänsch, Felix A1 - Daniel, Mirco A1 - Lanig, Harald A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - An automated calculation pipeline for differential pair interaction energies with molecular force fields using the Tinker Molecular Modeling Package JF - Journal of Cheminformatics N2 - 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. KW - Intermolecular interaction KW - Nonbonding interaction KW - Molecular Force Field KW - Molecular modeling KW - Molecular Dynamics Y1 - 2024 U6 - https://doi.org/10.1186/s13321-024-00890-5 VL - 16 (2024) IS - Artikel Nr. 96 ER - TY - CHAP A1 - Zielesny, Achim A1 - Daniel, Mirco A1 - Lanig, Harald A1 - Steinbeck, Christoph T1 - An automated Calculation Pipeline for Differential Pair Interaction Energies with Molecular Force Fields using the Tinker Molecular Modeling Package T2 - 36th Molecular Modeling Workshop, Erlangen, Germany N2 - 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. Y1 - 2024 N1 - Poster Session, Abstract und Poster im Tagungsband veröffentlicht. ER - TY - CHAP A1 - Bänsch, Felix A1 - Daniel, Mirco A1 - Lanig, Harald A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - A Calculation Pipeline for Differential Molecule Pair Interaction Energies T2 - 35th Molecular Modeling Workshop Y1 - 2023 N1 - Poster Session, Abstract im Tagungsband veröffentlicht. 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 - JOUR A1 - Truszkowski, Andreas A1 - Daniel, Mirco A1 - Kuhn, Hubert A1 - Neumann, Stefan A1 - Steinbeck, Christoph A1 - Zielesny, Achim A1 - Epple, Matthias T1 - A molecular fragment cheminformatics roadmap for mesoscopic simulation JF - Journal of Cheminformatics Y1 - 2014 U6 - https://doi.org/10.1186/s13321-014-0045-3 SN - 1758-2946 VL - 6 IS - Artikelnr. 45 ER -