DECIMER: towards deep learning for chemical image recognition

Rajan, Kohulan; Zielesny, Achim; Steinbeck, Christoph

doi:10.1186/s13321-020-00469-w

Cited by 66 publications

(50 citation statements)

References 20 publications

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“…For a discussion of the problems of string tokenization for deep learning, we refer our readers to those two publications. Our results confirm the superiority of SELFIES for the task discussed here and in our work on Optical Chemical Entity Recognition [16]. Thus, for this work all SMILES strings were converted into SELFIES using a custom python script (Fig.…”

Section: Datasupporting

confidence: 84%

“…Using the CDK, explicit hydrogens were removed from the molecules and their topological structures were converted to canonical SMILES strings. The obtained 111 million molecules were filtered according to the ruleset of our previous DECIMER work [ 16 ], i.e. molecules must have a molecular weight of fewer than 1500 Da, not possess any counter ions, contain only C, H, O, N, P, S, F, Cl, Br, I, Se and B, not contain any hydrogen isotopes (D, T), have between 3 and 40 bonds, not contain any charged group, contain implicit hydrogens only, except in functional groups, …”

Section: Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 2 more Smart Citations

STOUT: SMILES to IUPAC names using neural machine translation

Rajan

Zielesny²,

Steinbeck

2021

J Cheminform

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Chemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International Union of Pure and Applied Chemistry (IUPAC) based on a set of rules. Due to the complexity of this ruleset a correct chemical name assignment remains challenging for human beings and there are only a few rule-based cheminformatics toolkits available that support this task in an automated manner. Here we present STOUT (SMILES-TO-IUPAC-name translator), a deep-learning neural machine translation approach to generate the IUPAC name for a given molecule from its SMILES string as well as the reverse translation, i.e. predicting the SMILES string from the IUPAC name. In both cases, the system is able to predict with an average BLEU score of about 90% and a Tanimoto similarity index of more than 0.9. Also incorrect predictions show a remarkable similarity between true and predicted compounds.

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Section: Datasupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

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STOUT: SMILES to IUPAC names using neural machine translation

Rajan

Zielesny²,

Steinbeck

2021

J Cheminform

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“…The DECIMER (Deep lEarning for Chemical IMagE Recognition) project [ 18 ] is an end-to-end open-source system that can perform chemical structure segmentation on scanned scientific literature and use the segmented structure depictions to convert them into a computer-readable molecular file format.…”

Section: Introductionmentioning

confidence: 99%

DECIMER 1.0: deep learning for chemical image recognition using transformers

Rajan

Zielesny²,

Steinbeck

2021

J Cheminform

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The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50–100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.

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“…With the DECIMER [ 9 ] project, we are currently working on the development of an open-source platform for the automated chemical structure extraction from printed literature. It aims at segmenting all chemical structure depictions from a given scanned document from the printed scientific literature and resolving their identity to yield a machine-readable presentation of the molecule.…”

Section: Introductionmentioning

confidence: 99%

DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature

et al. 2021

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Chemistry looks back at many decades of publications on chemical compounds, their structures and properties, in scientific articles. Liberating this knowledge (semi-)automatically and making it available to the world in open-access databases is a current challenge. Apart from mining textual information, Optical Chemical Structure Recognition (OCSR), the translation of an image of a chemical structure into a machine-readable representation, is part of this workflow. As the OCSR process requires an image containing a chemical structure, there is a need for a publicly available tool that automatically recognizes and segments chemical structure depictions from scientific publications. This is especially important for older documents which are only available as scanned pages. Here, we present DECIMER (Deep lEarning for Chemical IMagE Recognition) Segmentation, the first open-source, deep learning-based tool for automated recognition and segmentation of chemical structures from the scientific literature. The workflow is divided into two main stages. During the detection step, a deep learning model recognizes chemical structure depictions and creates masks which define their positions on the input page. Subsequently, potentially incomplete masks are expanded in a post-processing workflow. The performance of DECIMER Segmentation has been manually evaluated on three sets of publications from different publishers. The approach operates on bitmap images of journal pages to be applicable also to older articles before the introduction of vector images in PDFs. By making the source code and the trained model publicly available, we hope to contribute to the development of comprehensive chemical data extraction workflows. In order to facilitate access to DECIMER Segmentation, we also developed a web application. The web application, available at https://decimer.ai, lets the user upload a pdf file and retrieve the segmented structure depictions.

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DECIMER: towards deep learning for chemical image recognition

Cited by 66 publications

References 20 publications

STOUT: SMILES to IUPAC names using neural machine translation

STOUT: SMILES to IUPAC names using neural machine translation

DECIMER 1.0: deep learning for chemical image recognition using transformers

DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature

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