dataesr/scientific-paragraphs-categorization huggingface dataset:
Content Targets. A 'target' record in our database resolves to a UniProtKB/SwissProt Accession as its primary identifier. Since our 2016 publication  (5) , target expansion has been mainly for GtoImmuPdb. Specifically-added immunity, inflammation and infection targets include the following the pattern recognition receptors/proteins of the innate immune response: Immune checkpoint protein targets have been expanded and aggregated in new families, for example Immune checkpoint catalytic receptors (www. guidetopharmacology. org/ GRAC/FamilyDisplayForward?familyId=953) and 'Other immune checkpoint proteins' (www. guidetopharmacology. org/GRAC/FamilyDisplayForward?familyId=949). This allows us to capture the action of these proteins as both immune system regulators and as drug targets. Another new target family is Butyrophilin and butyrophilin-like proteins were added for their increasingly indicated roles in gamma-delta T cells  (14).
As previously, ligands without an experimentally-verified molecular mechanism of action, but where potential efficacy is clearly described in papers, are also captured. There are new ligand groups, for example Immune checkpoint modulators (www. guidetopharmacology. org/GRAC/ FamilyDisplayForward?familyId=969), which group disparate ligands with common functionality. We have extended the database schema to group some ligands into families using the information on Hugo Gene Nomenclature Committee (HGNC) gene family pages (www. genenames. org/cgi-bin/genefamilies/) (15, 16) ; examples include cytokines and chemokines. A new family listing page Interactions and database growth. Table  1C  and Figure  2  show the expansion of curated interaction data across targets and ligands. Most targets added over the past two years fall into the 'Enzyme' and 'Other protein' target categories.
PubChem content. Our PubChem integration strategy has been previously outlined  (5). Since 2015 we have made nine PubChem submissions for new releases of our database. For 2017. 5 (see release notes https://blog. guidetopharmacology. org/2017/08/22/ database-release-2017-5/) we now have 8978 Substance Identifiers (SIDs) (PubChem query 'IUPHAR/BPS Guide to PHARMACOLOGY'[SourceName]). We submit within days of our public release but it can take PubChem a few days to process a new submission and several weeks to complete the more computationally intensive relationship mappings (e. g. 3D neighbours). Figure  3  shows an analysis of our content in Pub-Chem. For the SIDs (Figure  3A ), we have extended our SID comments to be able to identify 'approved drug' and 'immunopharmacology' subsets. Submission to Pub-Chem allows users the useful ability to compare different sources, using filters for 'slicing and dicing'  (17).
Doing so highlights our complementarity to other sources, such as GtoPdb having 1595 CID structures that ChEMBL does not (Figure  3B ). The same figure shows that in the case of both DrugBank  (18)  and DrugCentral  (19)  there are ∼5500 CIDs unique to just GtoPdb. A more detailed discussion of GtoPdb PubChem content and comparisons to other chemical sources can be found in our blog (https://blog. guidetopharmacology. org/2017/10/ 18/gtopdb-nar-database-issue-2018-pubchem-content/).
To access SAR data for all targets, we point users to our download page (www. guidetopharmacology. org/download. jsp) where they can download a CSV file containing all interactions. Visualising ligand activity across species. Many pharmacological parameters are reported in results sections of papers and cannot easily be found by searching   (22). The plots for each species are given a unique colour, which allows species differences to be quickly visualized (Figure  4 ). Further information on assay details, references, PubMed links and source databases are in tables below the graphs. GtoPdb ligand binding data are supplemented with additional values from ChEMBL (currently version 23, updated when a new ChEMBL version is released) because, while GtoPdb primarily focuses on human targets with some supporting rodent data, ChEMBL covers a wider range of species reported in medicinal chemistry literature.
NC-IUPHAR and GtoPdb faculty pages. NC-IUPHAR subcommittees reviewing GtoPdb data have, over 14 years, recorded contributions from over 800 scientists. To recognize their invaluable contributions, we have added contributor faculty pages, with individual profiles listing details of subcommittee membership and database contributions, institutional address, ORCIDs and external profile links such as departmental home pages, reference lists and social network profiles. The information is all entirely optional and provided by the individual, so some profiles are more complete than others. Contributor profiles may be accessed by clicking on a contributor's name in citations, on the contributor list (www. guidetopharmacology. org/ GRAC/ContributorListForward) or via a database search. The new faculty pages also provide users with greater insight into the data review process and roles of subcommittees.
GUIDE TO IMMUNOPHARMACOLOGY The Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb) expansion has extended the database to incorporate data types relevant to immunopharmacology, and associate these with existing target and ligand entities. This enrichment includes data on immunological processes or pathways, cell types of relevance to immunology, and diseases related to the immune/inflammatory system. We have already described the new targets and ligands included as part of GtoImmuPdb. Here we describe methodology of tagging new and existing targets and ligands along with the major data type expansions.
These have an approximately equal split between context papers (target reviews and mechanistic immunology, e. g. (http://www. citeulike. org/user/efaccenda/article/14420478)) and those specifically designated for subsequent curatorial extraction. The former are collated under an immunopharmacology further reading page (http://www. guidetoimmunopharmacology. org/GRAC/ ImmunoFurtherReadingForward). For the latter, precuration notes are added in the comments section (e. g. http://www. citeulike. org/user/cdsouthan/article/14414220), with links to the database added post-curation. The 'curation queue' can be viewed at (http://www. citeulike. org/user/ cdsouthan/tag/tobecurated). In setting up GtoImmuPdb we had many detailed curatorial aspects to consider but four are especially important. The first is that there are no unique ligand or target entities since these are all subsumed into GtoPdb (and counted in our statistics sections).
iii) Using GO annotations and literature reviews to prioritize immunological targets for curation (see below). The counts of GtoImmuPdb tagged targets and ligands are shown in Table  1 , with the full list provided on the website (http://www. guidetoimmunopharmacology. org/ immuno/immunoHelpPage. jsp#downloads). Curators continually review literature covering immunology and inflammation using the triage methodology described above. New targets and ligands are added to the database as new evidence emerges. We also review clinical developments in immunity/inflammation/immunooncology fields. This includes evaluating clinical development pipelines, identifying lead compounds, their molecular targets and pharmacological data (which can involve interrogation of patent literature where peer reviewed disclosure is not yet available).
This is illustrated in Figure  5  which shows the parent GO terms (and counts of child terms) mapped to the T cell activation process category. GO terms and IDs related to immune and inflammatory processes were obtained from the GO Consortium (http://purl. obolibrary. org/obo/go. obo) and used to populate database tables. We captured all is a relationships between the terms to support inferred searching by GO terms. This means a search on a parent term will also include any of its children. Target-GO annotations were downloaded from UniPro-tKB (2017 08 release)  (28)  and parsed into the database, along with GO evidence codes (www. geneontology. org/ page/guide-go-evidence-codes). GO evidence code indicate the level of support for annotations to terms, which allows users and curators to judge the strength of the evidence for the association.
Associations where the evidence is tenuous, such as for the IEA (Inferred by Electronic Analysis) code, can be split (this is the only evidence code that is assigned automatically by GO, without curatorial judgement). The set of target-GO annotations was used to automatically assign targets to the top-level categories. Our curators then reviewed the annotations. 2 ). tions can be browsed from the Immuno Process association pages (www. guidetoimmunopharmacology. org/GRAC/ ProcessesForward). In order to keep GO annotations up-to-date with the latest UniProtKB release, we have semi-automated scripts that are run upon each database release.
The GtoPdb database contains over 2000 disease terms (including synonyms). We have so far matched over 300 to ligands of immunological relevance. Curation of targets against immunological disease is in its infancy, but already contains associations to 17 immunological diseases. As expected, these include those under particularly active investigation (as judged by the current literature output), such as asthma, rheumatoid arthritis, inflammatory bowel disease, chronic obstructive pulmonary disease, sepsis and fibrosis. A full breakdown of the disease associations can be viewed at www. guidetoimmunopharmacology. org/GRAC/ ImmunoDiseaseListForward?type=target.
WEBSITE The GtoPdb website has been extended to surface new data types and incorporate them into existing search and browse mechanisms. We have not developed a new resource, but have extended the existing database and website with a new 'layer' of immunological data and an interface for immunologist users, the 'GtoImmuPdb view' of the data. This view highlights content of immunological relevance and prioritizes immunological data in search results and displays. In addition, we created a new entry portal (at www. guidetoimmunopharmacology. org) which provides access points to immunological data. At this stage, the Guide to IMMUNOPHARMACOL-OGY is in beta-release (v2. 0): although it contains most features and functionality expected in the full public release (due in 2018), it remains under development and may contain minor bugs and/or portions not yet optimized.
Portal The GtoImmuPdb portal is intended to provide a new access point to the database, with quick access to the data types of most relevance to immunopharmacology. The portal is styled after its parent GtoPdb, but with its own distinctive logo, header and colour scheme. This ensures that it is intuitively familiar to GtoPdb users while at the same time maintaining its own identity. We have also used the new colour scheme to highlight content of relevance to immunopharmacology within the existing GtoPdb interface. In several places toggle buttons allow users to switch between the new GtoImmuPdb view and the existing GtoPdb view (see Figure  1 ). The portal provides easy access to the new data types via panels on the portal home page. Detailed descriptions of navigating and viewing data in GtoImmuPdb are given in the next few sections.
Searching GtoImmuPdb Search mechanisms have been extended to incorporate all additional immunological data, including process, cell type and disease terms, ontological definitions and ontology IDs. We have also modified the search algorithm when searching via the GtoImmuPdb portal to increase the ranking of results likely to be of greater immunological relevance. We applied a weighting to certain database fields so that entities matching on those fields will appear higher up the list of results. The weighting is only applied when searching from a GtoImmuPdb page, not from the standard GtoPdb pages and is based on the amount of immunological data curated against an entity. For example, targets that have process, cell type and disease data annotated against them will rank higher than targets with only process data.
ELIXIR ELIXIR is the European infrastructure established specifically for the sharing and sustainability of life science data (www. elixir-europe. org/). The UK node, ELIXIR-UK (www. elixir-uk. org/), expanded in 2015/16, sought to identify resources that were representative of the UK bioinformatics community  (33)  and bring the benefit of participation to a wider range of UK institutions. Falling within ELIXR's strategic theme of human health and disease, GtoPdb already has links with other ELIXIR resources, including UniProtKB (where we regularly update crossreferences), and is in a good position to interoperate with other resources through our use of standard ontologies and identifiers (UniProtKB Accessions, nomenclature (as assigned by NC-IUPHAR committees in collaboration with HGNC), Ensembl IDs, PubMed IDs and PDB IDs).
RDF Over the last year, work has been carried out to design and implement an RDF version of a subset of the GtoPdb dataset, now available from (www. guidetopharmacology. org/downloads. jsp). The RDF data generation has been focussed on the main target-ligand interaction data. To ensure FAIR compliance machine-readable metadata have been generated in accordance with the W3C Health Care and Life Sciences Community (HCLS) Profile  (35). As an example of RDF modelling, the interaction between the target 5-HT 1A receptor and the ligand Ipsapirone is shown in Figure  8. In addition to the already stated aim of making GtoPdb FAIR compliant, it was also planned as a way to include the GtoPdb data into the Open PHACTS Drug Discovery Platform  (36, 37). We have developed an ontology (http://rdf. guidetopharmacology.
The RDF data are generated from the corresponding released version of the GtoPdb database. This ensures that the RDF data are consistent with the database content and do not require the maintenance of a second database. Three relationalto-RDF (R2RM, (41)) mapping files have been created that control the generation of the RDF data--one each for targets, ligands and interactions respectively. These are available from https://github. com/HW-SWeL/GTP-RDF/tree/master/Morph/Guide2Pharma/R2RML. We execute the mappings using the Morph RDB framework  (42). Again, following best practice  (43) , a table identifier scheme has been established for the RDF data. Another key aspect of the generation process is the creation of machinereadable metadata. The metadata capture the provenance information between the RDF version of the data and the relational database version that was used to create it.
The metadata comply with the W3C HCLS Community Profile to the SHOULD level and this has been verified with the Validata tool (http://hw-swel. github. io/Validata). We are in the process of providing linked data pages for each resource in the RDF data, i. e. the URL used to identify each target, ligand and interaction in the RDF will become dereferenceable. Additionally, we will be providing a SPARQL endpoint with a set of example queries to help exploit the RDF data and enable deeper analysis of the GtoPdb data in conjunction with other linked data datasets. In the future, we will provide link-sets that capture the database cross-references contained in the GtoPdb as well as extend coverage of the data.
Database links The current set of database link-outs from GtoPdb target and ligand pages can be viewed at www. guidetopharmacology. org/helpPage. jsp#databaseLinks. New links since 2016 on the ligand side include UniChem, which cross-references between chemical structure identifiers from different databases  (44). On the target side, there are links to the SLC Transporter Database (http://slc. bioparadigms. org/) with information on transporter functional genomics, CATH structural domains and superfamilies  (45)  and the Human Protein Atlas tissue expression database  (46) , these latter two being ELIXIR core resources  (47). We have also ensured cross-links exist between GtoPdb and the Pharmacology Education Website (PEP, www. pharmacologyeducation. org/) and SynPharm (http://synpharm. guidetopharmacology.
org/), the database of ligand-binding sequences. We ensure that the cross-links are regularly refreshed through formal and informal contacts with database providers. Of note are the links to RCSB Protein Data Bank (PDB) (www. rcsb. org) (48) ligands. The acceleration at which pharmacological targets are being co-crystallized with ligands caused us to introduce PDB InChIKey lookups as part of our database update procedure, to ensure that links are up-to-date in each release. Consequent to the creation of peptide ligand family pages, we also updated our reciprocal HGNC links to include human genes encoding peptide ligands. We encourage in-links from other resources, although it is difficult to discern the extent of these unless we are directly informed or cited. A recent survey (https://blog.
Journal to database connectivity GtoPdb has continued to enhance its connectivity to journals. Much of our endeavours since our last update (5) have been described in our blog (https://blog. guidetopharmacology. org/2017/10/18/ gtopdb-nar-database-issue-2018-journal-to-databaseconnectivity-and-journal-to-gtopdb-links/). This includes details on PubMed ID statistics in curated GtoPdb content. The same post also shows how engagement with British Journal of Pharmacology (BJP) has extended the previous provision of live out-links  (50) , to now include in line links, rather than the previous method of adding separate tables to manuscripts.
External profile We use a number of strategies to disseminate information about GtoPdb. Our NC-IUPHAR newsletter is prepared in sync with database releases and sent to subscribers and committee members with articles about the database and hot topics in pharmacology. Social media (Twitter, Facebook and LinkedIn) is one of our primary methods of outreach to announce database updates, publications of interest, upcoming events and IUPHAR reviews. Our blog (https://blog. guidetopharmacology. org/) is used to provide detailed descriptions of database releases, technical updates and commentary on hot topics in pharmacology. For example, the development of the Guide to IMUNOPHAR-MACOLOGY is described in a series of technical blog posts (https://blog. guidetopharmacology. org/category/ guide-to-immunopharmacology/).
FUTURE DIRECTIONS Guide to IMMUNOPHARMACOLOGY is in its beta phase: we expect the portal and website to be completed by Spring 2018 when the first full public release will be available. At present, most features of the interface are in place. We have undertaken a short phase of user-testing; an exercise we plan to repeat before the full public release. This will identify any aspects of the design and navigation that need improvement, and features that users would like to see implemented. In response to previous user feedback we will also consider a graphical-based navigation system to improve user-friendliness and enhance the look of the website. New data curation will continue until Autumn 2018, with a focus on target associations to disease and cell types that D1104 Nucleic Acids Research, 2018, Vol.
46, Database issue are currently under-represented in the database. We will also continue to assign further targets to top-level immunological process categories by looking at their GO annotations and supporting evidence. We will continue the development of our RDF platform with short-term priorities of establishing a linked data explorer and a SPARQL end-point to enable queries across the data. Making the RDF publicly available allows integration into other semantic databases (both proprietary and non-proprietary). We are also working with Bioschemas (http://bioschemas. org/) to add schema. org semantic mark-up to GtoPdb, which would make it easier for search engines to find, collate and analyse the data  (51). Many of the main use-cases and features in GtoPdb will be elaborated on in a forthcoming paper in Current Protocols in Bioinformatics (manuscript in preparation).
A new, sister resource to the main GtoPdb called Syn-Pharm (http://synpharm. guidetopharmacology. org/) has been developed as a database of drug-responsive protein sequences, derived from the interactions in GtoPdb and using data from the RCSB PDB. SynPharm is an open-access, web-based tool that integrates pharmacological and ligandprotein binding information to present data on the drugbinding domains of proteins in a manner useful to synthetic biologists. It will be kept up-to-date in line with GtoPdb and we are preparing a paper detailing the resource. mendations for accessing and downloading data, and linking to us remain as reported in 2016. We provide various options for download, and users are welcome to contact us (enquiries@guidetopharmacology. org) for additional datasets and file formats.
We provide a dump file of the full PostgreSQL database (http://www. postgresql. org/) but we no longer provide a MySQL version as standard (although we would be glad to provide this on request). We also provide RDF flat files as described above (available from www. guidetopharmacology. org/downloads. jsp), which users can load into a local triple store and perform SPARQL queries across the data. Our REST web services are available at http://www. guidetopharmacology. org/ webServices. jsp and provide computational access to data in JavaScript Object Notation (JSON) format. In response to user feedback we recently expanded and revised our web services and made speed improvements. We added services to allow direct querying of interaction data and references. For example, users can now obtain a complete set of target-ligand interactions with many options to filter this list, including by target or ligand type, binding affinity, drug approval status or chemical structure.
An interaction is only considered part of GtoImmuPdb where both the target and ligand are tagged as relevant to immunopharmacology. The table shows just under one quarter of all the curated interactions in GtoPdb involve targets and ligands of immunological relevance (we are still in the process of identifying these so it is likely to increase). The table includes a comparison to the figures from the 2016 update (5), and a breakdown for the GtoImmuPdb dataset. Categories are not mutually exclusive and targets and ligands can fall into more than one, therefore totals are not the sum of all other rows.
ACKNOWLEDGEMENTS The authors wish to thank all members of  NC-IUPHAR  for their continued support (http://www. guidetopharmacology. org/nciuphar. jsp#membership). This includes the following members who are not already authors listed on this manuscript:  E. A. Bruford ,  A. Christopoulos ,  J. A. Cidlowski ,  S. Enna ,  K. Kaibuchi ,  Y. Kanai ,  E. H. Ohlstein ,  J. A Peters ,  A. Phipps  and  J. Striessnig. We also thank the global network of NC-IUPHAR subcommittees and all the CGTP contributors (a full list of subcommittee members and contributors can be viewed at http://www. guidetopharmacology. org/GRAC/ ContributorListForward). We thank  T. Wigglesworth  for administrative support. We thank  O. P. Buneman  for technical advice and help in producing the CGTP. In addition to our primary funding, we are grateful for sponsorship from the  American Society for Pharmacology and Experimental Therapeutics (ASPET) ,  Laboratoires Servier  and  The University of Edinburgh.
DATA ACCESS GtoPdb and GtoImmuPdb are available online at http://www. guidetopharmacology. org and http://www. guidetoimmunopharmacology. org, r espectively. Both are licensed under the Open Data Commons Open Database License (ODbL) (http://opendatacommons. org/licenses/odbl/), and the contents are licensed under the Creative Commons Attribution-ShareAlike 3. 0 Unported license (http://creativecommons. org/licenses/by-sa/3. 0/). Recom-.