Abstract
The Molecular Entities in Linked Data (MEiLD) dataset comprises data of distinct atoms, molecules, ions, ion pairs, radicals, radical ions, and others that can be identifiable as separately distinguishable chemical entities. The dataset is provided in a JSON-LD format and was generated by the SDFEater, a tool that allows parsing atoms, bonds, and other molecule data. MEiLD contains 349,960 of ‘small’ chemical entities. Our dataset is based on the SDF files and is enriched with additional ontologies and line notation data. As a basis, the Molecular Entities in Linked Data dataset uses the Resource Description Framework (RDF) data model. Saving the data in such a model allows preserving the semantic relations, like hierarchical and associative, between them. To describe chemical molecules, vocabularies such as Chemical Vocabulary for Molecular Entities (CVME) and Simple Knowledge Organization System (SKOS) are used. The dataset can be beneficial, among others, for people concerned with research and development tools for cheminformatics and bioinformatics. In this paper, we describe various methods of access to our dataset. In addition to the MEiLD dataset, we publish the Shapes Constraint Language (SHACL) schema of our dataset and the CVME ontology. The data is available in Mendeley Data.
Highlights
The Molecular Entities in Linked Data (MEiLD) dataset comprises data of distinct atoms, molecules, ions, ion pairs, radicals, radical ions, and others that can be identifiable as separately distinguishable chemical entities
Computer science (Information Systems) Semantic Web, Linked Data Graph Document data was acquired by fetching available public domain documents and generated by a software
In Chemical Vocabulary for Molecular Entities (CVME), molecular entities are modeled as instances of the class cvme:MolecularEntity, which is a subclass of skos:Concept
Summary
Computer science (Information Systems) Semantic Web, Linked Data Graph Document data was acquired by fetching available public domain documents and generated by a software. The presented dataset of molecular entities is useful because it includes a classification, whereby the relationships between molecular entities and their parents and/or children are described. The provided dataset is useful, because all chemicals in the dataset contain a subsumption relationship, meaning that all of the molecular entries are available to semantic reasoning tools that harness the classification hierarchy. The dataset may be beneficial for the users of information services and systems, along with those who use them through query or inference operations. Resources can be described in collaboration with other datasets and linked to data contributed by other communities
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