Exploiting minable connectivity from The IUPHAR/BPS Guide to PHARMACOLOGY to an expanding range of ligands, targets, publications and patents

Abstract Introduction The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; www.guidetopharmacology.org) (1), is an open-access, expert-curated, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) online database of pharmacological targets and the substances that act on them. Its reputation for quality is recognised by a Hidden REF award and selection as both an ELIXIR-UK and Global Core Biodata Resource. We continue to expand connectivity of our curated entities to external resources in other quality databases. These consequently offer an expanding range of powerful navigation and data mining options across chemical structures, sequences and documents that users may not be aware of but will be outlined Methods GtoPdb regularly submits ligand substances (SIDs) to PubChem the majority of which have defined chemical structures (CIDs) and a range of useful mappings to many PubChem resources. We have introduced SID tagging for uniquely specific ligand sets that can be used for further analyses. For targets we maintain a UniProt cross-reference set. For journal publications we have enabled the NCBI to provide LinkOuts to all our curated references and also submitted a subset of these that document quantitative interaction data to European PubMed Central (EPMC) Results Of the 12,744 GtoPdb substances 10565 form CIDs that are connected by identity, extensive annotations and similarity neighbours within the vast PubChem system. Useful intersects include 86% BioAssay results, 83% to patent documents, 23% to PDB entries and 82% vendor offerings. Our expanding SID tags retrieve 1995 approved drugs, 393 clinical antibodies, 136 entries to the Guide to Malaria Pharmacology (GtoMPdb), 1477 from Guide to Immunopharmacology (GtoImmuPdb), 561 antibacterials and 355 natural products. For literature connectivity NCBI LinkOut includes 33,093 GtoPdb-indexed PubMed IDs. Of these 8,418 are indexed in EPMC. The GtoPdb chemistry-to-target cross-references in UniProt are up to 2,272 sequences including Human (1,647), Rat (323) and Mouse (278). Conclusions GtoPdb provides users with expanding connectivity out to the pharmacology informatics ecosystem. While querying and navigating between ligands, documents and targets is complex the ability to “slice-dice-and-compare” across these key entities is powerful and provides unique insights. We are now documenting how to exploit this connectivity in GtoPdb. References 1. Harding SD, Armstrong JF, Faccenda E, Southan C, Alexander SPH, Davenport AP, Spedding M, Davies JA. The IUPHAR/BPS Guide to PHARMACOLOGY in 2024. Nucleic Acids Res. 2024 Jan 5;52(D1):D1438-D1449

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Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-29
DOI
https://doi.org/10.5281/zenodo.14246696
Primary Topic
Computational Drug Discovery Methods
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article
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article

Exploiting minable connectivity from The IUPHAR/BPS Guide to PHARMACOLOGY to an expanding range of ligands, targets, publications and patents

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Exploiting minable connectivity from The IUPHAR/BPS Guide to PHARMACOLOGY to an expanding range of ligands, targets, publications and patents

article en

Abstract

Abstract Introduction The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; www.guidetopharmacology.org) (1), is an open-access, expert-curated, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) online database of pharmacological targets and the substances that act on them. Its reputation for quality is recognised by a Hidden REF award and selection as both an ELIXIR-UK and Global Core Biodata Resource. We continue to expand connectivity of our curated entities to external resources in other quality databases. These consequently offer an expanding range of powerful navigation and data mining options across chemical structures, sequences and documents that users may not be aware of but will be outlined Methods GtoPdb regularly submits ligand substances (SIDs) to PubChem the majority of which have defined chemical structures (CIDs) and a range of useful mappings to many PubChem resources. We have introduced SID tagging for uniquely specific ligand sets that can be used for further analyses. For targets we maintain a UniProt cross-reference set. For journal publications we have enabled the NCBI to provide LinkOuts to all our curated references and also submitted a subset of these that document quantitative interaction data to European PubMed Central (EPMC) Results Of the 12,744 GtoPdb substances 10565 form CIDs that are connected by identity, extensive annotations and similarity neighbours within the vast PubChem system. Useful intersects include 86% BioAssay results, 83% to patent documents, 23% to PDB entries and 82% vendor offerings. Our expanding SID tags retrieve 1995 approved drugs, 393 clinical antibodies, 136 entries to the Guide to Malaria Pharmacology (GtoMPdb), 1477 from Guide to Immunopharmacology (GtoImmuPdb), 561 antibacterials and 355 natural products. For literature connectivity NCBI LinkOut includes 33,093 GtoPdb-indexed PubMed IDs. Of these 8,418 are indexed in EPMC. The GtoPdb chemistry-to-target cross-references in UniProt are up to 2,272 sequences including Human (1,647), Rat (323) and Mouse (278). Conclusions GtoPdb provides users with expanding connectivity out to the pharmacology informatics ecosystem. While querying and navigating between ligands, documents and targets is complex the ability to “slice-dice-and-compare” across these key entities is powerful and provides unique insights. We are now documenting how to exploit this connectivity in GtoPdb. References 1. Harding SD, Armstrong JF, Faccenda E, Southan C, Alexander SPH, Davenport AP, Spedding M, Davies JA. The IUPHAR/BPS Guide to PHARMACOLOGY in 2024. Nucleic Acids Res. 2024 Jan 5;52(D1):D1438-D1449

Zenodo (CERN European Organization for Nuclear Research)
University of Edinburgh (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 63%
Computational Drug Discovery Methods
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