Background: Making data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes the data machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis.
Results: This paper describes recent developments in an ongoing project converting data from the ChEMBL database into RDF triples. Relative to earlier versions, this updated version of ChEMBL-RDF uses recently introduced ontologies, including CHEMINF and CiTO; exposes more information from the database; and is now available as dereferencable, linked data. To demonstrate these new features, we present novel use cases showing further integration with other web resources, including Bio2RDF, Chem2Bio2RDF, and ChemSpider, and showing the use of standard ontologies for querying.
Conclusions: We have illustrated the advantages of using open standards and ontologies to link the ChEMBL database to other databases. Using those links and the knowledge encoded in standards and ontologies, the ChEMBL-RDF resource creates a foundation for integrated semantic web cheminformatics applications, such as the presented decision support.
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http://dx.doi.org/10.1186/1758-2946-5-23 | DOI Listing |
ACS Med Chem Lett
December 2024
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC values.
View Article and Find Full Text PDFUnlabelled: Hematological cancer treatment with hybrid kinase/HDAC inhibitors is a novel strategy to overcome the challenge of acquired resistance to drugs. We collected IC datasets from the ChEMBL database for 13 cancer cell lines (72 h cytotoxicity, measured by MTT), known inhibitors for 38 kinases, and 10 HDACs isoforms, that we identified by target fishing and literature review. The data was subjected to rigorous biological and chemical curation leaving the final datasets ranging from 76 to 8173 compounds depending on the target.
View Article and Find Full Text PDFACS Omega
December 2024
Department of Biochemistry and Molecular Biology, College of Medicine, Chang Gung University, Kweishan, Taoyuan 333, Taiwan.
Influenza A and B viruses spread out worldwide, causing several global concerns. Discovering neuraminidase inhibitors to prevent influenza A and B viruses is thus of great interest. In this work, a machine learning model was trained and tested to evaluate the ligand-binding affinity to neuraminidase.
View Article and Find Full Text PDFToxicology
December 2024
Department of Orthopaedics,The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, 646000, China. Electronic address:
The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (ATBC), may adversely affect thyroid health, yet the precise mechanisms remain poorly understood. The objective of this study was to elucidate the complex effects of acetyl tributyl citrate (ATBC) on the thyroid gland and to clarify the potential molecular mechanisms by which environmental pollutants influence the disease process.
View Article and Find Full Text PDFMol Inform
December 2024
Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4, Blaise Pascal Str., 67000, Strasbourg, France.
Dimensionality reduction is an important exploratory data analysis method that allows high-dimensional data to be represented in a human-interpretable lower-dimensional space. It is extensively applied in the analysis of chemical libraries, where chemical structure data - represented as high-dimensional feature vectors-are transformed into 2D or 3D chemical space maps. In this paper, commonly used dimensionality reduction techniques - Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) - are evaluated in terms of neighborhood preservation and visualization capability of sets of small molecules from the ChEMBL database.
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