DrugCentral 2023 extends human clinical data and integrates veterinary drugs

Avram, Sorin; Wilson, Thomas B.; Curpăn, Ramona; Halip, Liliana; Borota, Ana; Bora, Alina; Bologa, Cristian; Holmes, Jayme; Knockel, Jeffrey; Yang, Jeremy J.; Oprea, Tudor I.

doi:10.1093/nar/gkac1085

Cited by 30 publications

(14 citation statements)

References 55 publications

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“…Drug related databases. DrugBank [1] and DrugCentral [36] are comprehensive platforms for drugs and drug-related entities. BioSNAP [37] contains drug-disease information.…”

Section: Data Sources To Construct Megakgmentioning

confidence: 99%

MegaKG: Toward an explainable knowledge graph for early drug development

Dong,

Liu,

Wei

et al. 2024

Preprint

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In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and 9, 165, 855 edges/relations after stringent data processing. Such a massive KG can not only provide a holistic view of the entities of interest, but also generate insightful hypotheses on unknown relations by applying AI computations. We focused on the interplay of the key elements in drug development, such as genes, diseases and drugs, and aimed to facilitate practical applications that could benefit early drug development in industries. More importantly, we placed much emphasis on the exploitability of the predictions generated by MegaKG. This may greatly help researchers to assess the feasibility or design appropriate downstream validation experiments, making AI techniques more than just black-box models. In this regard, NBFNet was adopted, which combines the advantages of both traditional path-based methods and more recently developed GNN-based ones. Performance evaluation experiments indicated superior results by MegaKG. We also conducted real case studies to validate its practical utility in various scenarios, including target prediction, indication extension and drug repurposing. All these experiments highlighted the potential of MegaKG as a valuable tool in driving innovation and accelerating drug development in pharmaceutical industry.

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“…Drug related databases. DrugBank [1] and DrugCentral [36] are comprehensive platforms for drugs and drug-related entities. BioSNAP [37] contains drug-disease information.…”

Section: Data Sources To Construct Megakgmentioning

confidence: 99%

MegaKG: Toward an explainable knowledge graph for early drug development

Dong,

Liu,

Wei

et al. 2024

Preprint

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“…In the same area ChemFOnt ( 84 ) brings a new hierarchical ontology describing functions of biologically important chemicals. Major returning resources for drugs and their targets include DrugCentral ( 85 ), which now covers veterinary drugs too and has new data on adverse drug events, and TCRD/Pharos ( 86 ) which has incorporated fresh data sources and new data visualisations such as clever circular treemaps to intuitively map expression onto ontologies. The toxicological properties of drugs and other chemicals are covered by the popular CTD ( 87 ) which introduces ‘CTD tetramers’, information blocks containing chemical, gene, phenotype and disease compiled from pairwise interactions; and the newcomer TOXRIC ( 88 ), which is especially notable for offering standardised benchmarks ready for use by Machine Learning methods.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Rigden

Fernández

2023

Nucleic Acids Research

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The 2023 Nucleic Acids Research Database Issue contains 178 papers ranging across biology and related fields. There are 90 papers reporting on new databases and 82 updates from resources previously published in the Issue. Six more papers are updates from databases most recently published elsewhere. Major nucleic acid databases reporting updates include Genbank, ENA, ChIPBase, JASPAR, mirDIP and the Issue's first Breakthrough Article, NACDDB for Circular Dichroism data. Updates from BMRB and RCSB cover experimental protein structural data while AlphaFold 2 computational structure predictions feature widely. STRING and REBASE are stand-out updates in the signalling and enzymes section. Immunology-related databases include CEDAR, the second Breakthrough Article, for cancer epitopes and receptors alongside returning IPD-IMGT/HLA and the new PGG.MHC. Genomics-related resources include Ensembl, GWAS Central and UCSC Genome Browser. Major returning databases for drugs and their targets include Open Targets, DrugCentral, CTD and Pubchem. The EMPIAR image archive appears in the Issue for the first time. The entire database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, revisiting 463 entries, adding 92 new resources and eliminating 96 discontinued URLs so bringing the current total to 1764 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

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“…PubChem includes over 100 million compounds and 300 million substances . Other drug-related information can be obtained through specialized databases and software, including ATC/DDD Index, DrugCentral, Open Babel, and PaDEL-Descriptor …”

Section: Concepts and Data Sources Of Networkmentioning

confidence: 99%

Network-Based Methods and Their Applications in Drug Discovery

Yu,

Wu,

Wang

et al. 2023

J. Chem. Inf. Model.

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Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of networkbased methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

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DrugCentral 2023 extends human clinical data and integrates veterinary drugs

Cited by 30 publications

References 55 publications

MegaKG: Toward an explainable knowledge graph for early drug development

MegaKG: Toward an explainable knowledge graph for early drug development

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Network-Based Methods and Their Applications in Drug Discovery

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