Automated systematic nomenclature generation for organic compounds

Williams, Antony J.; Yerin, Andrey

doi:10.1002/wcms.1118

Cited by 4 publications

(3 citation statements)

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“…Although the latest IUPAC nomenclature for organic chemistry [ 1 ] describes a method for choosing a preferred IUPAC name, it is incomplete and difficult to apply algorithmically. As a result, the names generated by commercial packages are often in disagreement [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

et al. 2021

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We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an encoder-decoder architecture, a setup similar to the neural networks used in state-of-the-art machine translation. Unlike neural machine translation, which usually tokenizes input and output into words or sub-words, our model processes the InChI and predicts the IUPAC name character by character. The model was trained on a dataset of 10 million InChI/IUPAC name pairs freely downloaded from the National Library of Medicine’s online PubChem service. Training took seven days on a Tesla K80 GPU, and the model achieved a test set accuracy of 91%. The model performed particularly well on organics, with the exception of macrocycles, and was comparable to commercial IUPAC name generation software. The predictions were less accurate for inorganic and organometallic compounds. This can be explained by inherent limitations of standard InChI for representing inorganics, as well as low coverage in the training data.

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Section: Discussionmentioning

confidence: 99%

Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

et al. 2021

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“…158 Reviews on the opposite process, that is, conversion of structures into names, have also been published. 445 4.2. Chemical Entity Grounding 4.2.1.…”

Section: Name-to-structure Conversionmentioning

confidence: 99%

“…A similar approach combining dictionary-based methods and OPSIN is used by OSCAR . Reviews on the opposite process, that is, conversion of structures into names, have also been published …”

Section: Linking Documents To Structuresmentioning

confidence: 99%

Information Retrieval and Text Mining Technologies for Chemistry

et al. 2017

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Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

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Many InChIs and quite some feat

Warr

2015

J Comput Aided Mol Des

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Automated systematic nomenclature generation for organic compounds

Cited by 4 publications

References 20 publications

Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

Information Retrieval and Text Mining Technologies for Chemistry

Many InChIs and quite some feat

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